Method and apparatus for automatic visual event detection

ABSTRACT

Disclosed are methods and apparatus for automatic visual detection of events, for recording images of those events and retrieving them for display and human or automated analysis, and for sending synchronized signals to external equipment when events are detected. An event corresponds to a specific condition, among some time-varying conditions within the field of view of an imaging device, that can be detected by visual means based on capturing and analyzing digital images of a two-dimensional field of view in which the event may occur. Events may correspond to rare, short duration mechanical failures for which obtaining images for analysis is desirable. Events are detected by considering evidence obtained from an analysis of multiple images of the field of view, during which time moving mechanical components can be seen from multiple viewing perspectives.

RELATED APPLICATION

This application is a continuation-in-part of co-pending U.S. patentapplication Ser. No. 10/865,155, entitled METHOD AND APPARATUS FORVISUAL DETECTION AND INSPECTION OF OBJECTS, by William M. Silver, filedJun. 9, 2004, the teachings of which are expressly incorporated hereinby reference, and referred to herein as the “Vision Detector Method andApparatus”.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The invention relates to high-speed video event detection, motionanalysis, image recording, and automated image analysis.

2. Description of the Related Art

It is well-known in the art to use high-speed image recording devicesfor motion analysis of mechanical systems that operate too fast for thehuman eye to see. These devices capture and record hundreds or thousandsof images per second of some mechanical process, and then display thoseimages, in slow motion or as still pictures, for human users to see andanalyze the high-speed mechanical motions.

Of particular interest is recording rare, short-duration mechanicalevents that may cause failures in the mechanical process. The fact thatthese events are both rare and short-duration creates specialchallenges. Suppose, for example, that the image recording devicerecords 1000 images per second, the event lasts three milliseconds, andoccurs on average once an hour. Without some additional mechanism, thehuman user would need to look at, on average, 3.6 million pictures tofind the two or three that contain the event.

It is well-known in the art to address this challenge by providing atrigger signal for the image recording device that indicates when theevent has occurred. The image recording device keeps a limited number ofthe most recent images, say the last one second of recording, and whenthe trigger signal indicates that the event has occurred, records for abrief additional time and then stops. This gives the user a relativelysmall number of images to look at both before and after the event.Furthermore, the user knows exactly when each image was capturedrelative to the time of the event as indicated by the trigger signal.

Clearly, the success of this method depends on being able to generate asuitable trigger signal. It is well-known in the art to use aphotodetector for this purpose. A typical photodetector has a lightsource and a single photoelectric sensor that responds to the intensityof light that is reflected by a point on the surface of an object, ortransmitted along a path that an object may cross. A user-adjustablesensitivity threshold establishes a light intensity above which (orbelow which) an output signal of the photodetector will be energized.

It is often the case that multiple photodetectors are needed to providethe trigger signal. For example, if the mechanical process is amanufacturing line producing discrete objects, and the event correspondsto the production of an object with a missing component, then at leasttwo photodetectors are needed: one to detect that an object is present,and the other to detect the missing component. Sometimes even more thantwo are needed to detect complex events.

Using photodetectors to provide a trigger signal has some limitations,however, including

-   -   a simple measure of the intensity of light transmitted or        reflected by one or more points may be insufficient for        detecting the event;    -   it can be difficult to adjust the position of each photodetector        so that it is looking at the exact right point;    -   the points to be measured must not move around during normal        operation of the mechanical process; and    -   the need for multiple photodetectors can make installation and        setup difficult.

It is also known in the art to use a machine vision system to provide atrigger signal. A machine vision system is a device that can capture adigital image of a two-dimensional field of view, and then analyze theimage and make decisions. The image is captured by exposing atwo-dimensional array of photosensitive elements for a brief period,called the integration or shutter time, to light that has been focusedon the array by a lens. The array is called an imager and the individualelements are called pixels. Each pixel measures the intensity of lightfalling on it during the shutter time. The measured intensity values arethen converted to digital numbers and stored in the memory of the visionsystem to form the image, which is analyzed by a digital processingelement such as a computer, using methods well-known in the art to makedecisions.

A machine vision system can avoid the limitations of photodetectors. Onemachine vision system can replace many photodetectors and makesophisticated measurements of extended brightness patterns, instead ofjust single-point intensity measurements. Adjusting the positions lookedat can be done using a graphical user interface instead of a screwdriverand wrench, and those positions can be relocated for each image based onthe content of the image itself.

A machine vision system has its own limitations, however, including:

-   -   machine vision systems are generally only suitable when the        event relates to the inspection of discrete objects; and    -   machine vision systems are generally too slow to detect        short-duration events, and must instead look for some        long-duration condition caused by that event, such as a        defective product.

Note that when used to provide a trigger signal, a machine vision systemis separate from the high-speed image recording device. It does not seeand cannot analyze the images captured by that device, the very imagesthat contain the event that is to be detected. Even if those imagescould be made available to a machine vision system, they are produced atfar too high a rate to be analyzed by machine vision systems ofconventional design.

The Vision Detector Method and Apparatus teaches novel methods andsystems that can overcome the above-described limitations of prior artphotodetectors and machine vision systems for detecting that atriggering event has occurred. These teachings also provide fertileground for innovation leading to improvements beyond the scope of theoriginal teachings. In the following section the Vision Detector Methodand Apparatus is briefly summarized, and a subsequent section lays outthe problems to be addressed by the present invention.

Vision Detector Method and Apparatus

The Vision Detector Method and Apparatus provides systems and methodsfor automatic optoelectronic detection and inspection of objects, basedon capturing digital images of a two-dimensional field of view in whichan object to be detected or inspected may be located, and then analyzingthe images and making decisions. These systems and methods analyzepatterns of brightness reflected from extended areas, handle manydistinct features on the object, accommodate line changeovers throughsoftware means, and handle uncertain and variable object locations. Theyare less expensive and easier to set up than prior art machine visionsystems, and operate at much higher speeds. These systems and methodsfurthermore make use of multiple perspectives of moving objects, operatewithout triggers, provide appropriately synchronized output signals, andprovide other significant and useful capabilities that will be apparentto those skilled in the art.

One aspect of the Vision Detector Method and Apparatus is an apparatus,called a vision detector, that can capture and analyze a sequence ofimages at higher speeds than prior art vision systems. An image in sucha sequence that is captured and analyzed is called a frame. The rate atwhich frames are captured and analyzed, called the frame rate, issufficiently high that a moving object is seen in multiple consecutiveframes as it passes through the field of view (FOV). Since the objectsmoves somewhat between successive frames, it is located in multiplepositions in the FOV, and therefore it is seen from multiple viewingperspectives and positions relative to the illumination.

Another aspect of the Vision Detector Method and Apparatus is a method,called dynamic image analysis, for inspecting objects by capturing andanalyzing multiple frames for which the object is located in the fieldof view, and basing a result on a combination of evidence obtained fromeach of those frames. The method provides significant advantages overprior art machine vision systems that make decisions based on a singleframe.

Yet another aspect of the Vision Detector Method and Apparatus is amethod, called visual event detection, for detecting events that mayoccur in the field of view. An event can be an object passing throughthe field of view, and by using visual event detection the object can bedetected without the need for a trigger signal.

Additional aspects of the Vision Detector Method and Apparatus will beapparent by a study of the figures and detailed descriptions giventherein.

In order to obtain images from multiple perspectives, it is desirablethat an object to be detected or inspected moves no more than a smallfraction of the field of view between successive frames, often no morethan a few pixels. According to the Vision Detector Method andApparatus, it is generally desirable that the object motion be no morethan about one-quarter of the FOV per frame, and in typical embodimentsno more than 5% or less of the FOV. It is desirable that this beachieved not by slowing down a manufacturing process but by providing asufficiently high frame rate. In an example system the frame rate is atleast 200 frames/second, and in another example the frame rate is atleast 40 times the average rate at which objects are presented to thevision detector.

An exemplary system is taught that can capture and analyze up to 500frames/second. This system makes use of an ultra-sensitive imager thathas far fewer pixels than prior art vision systems. The high sensitivityallows very short shutter times using very inexpensive LED illumination,which in combination with the relatively small number of pixels allowsvery short image capture times. The imager is interfaced to a digitalsignal processor (DSP) that can receive and store pixel datasimultaneously with analysis operations. Using methods taught thereinand implemented by means of suitable software for the DSP, the time toanalyze each frame generally can be kept to within the time needed tocapture the next frame. The capture and analysis methods and apparatuscombine to provide the desired high frame rate. By carefully matchingthe capabilities of the imager, DSP, and illumination with theobjectives of the invention, the exemplary system can be significantlyless expensive than prior art machine vision systems.

The method of visual event detection involves capturing a sequence offrames and analyzing each frame to determine evidence that an event isoccurring or has occurred. When visual event detection is used to detectobjects without the need for a trigger signal, the analysis woulddetermine evidence that an object is located in the field of view.

In an exemplary method the evidence is in the form of a value, called anobject detection weight, that indicates a level of confidence that anobject is located in the field of view. The value may be a simple yes/nochoice that indicates high or low confidence, a number that indicates arange of levels of confidence, or any item of information that conveysevidence. One example of such a number is a so-called fuzzy logic value,further described therein. Note that no machine can make a perfectdecision from an image, and so will instead make judgments based onimperfect evidence.

When performing object detection, a test is made for each frame todecide whether the evidence is sufficient that an object is located inthe field of view. If a simple yes/no value is used, the evidence may beconsidered sufficient if the value is “yes”. If a number is used,sufficiency may be determined by comparing the number to a threshold.Frames where the evidence is sufficient are called active frames. Notethat what constitutes sufficient evidence is ultimately defined by ahuman user who configures the vision detector based on an understandingof the specific application at hand. The vision detector automaticallyapplies that definition in making its decisions.

When performing object detection, each object passing through the fieldof view will produce multiple active frames due to the high frame rateof the vision detector. These frames may not be strictly consecutive,however, because as the object passes through the field of view theremay be some viewing perspectives, or other conditions, for which theevidence that the object is located in the field of view is notsufficient. Therefore it is desirable that detection of an object beginswhen an active frame is found, but does not end until a number ofconsecutive inactive frames are found. This number can be chosen asappropriate by a user.

Once a set of active frames has been found that may correspond to anobject passing through the field of view, it is desirable to perform afurther analysis to determine whether an object has indeed beendetected. This further analysis may consider some statistics of theactive frames, including the number of active frames, the sum of theobject detection weights, the average object detection weight, and thelike.

The method of dynamic image analysis involves capturing and analyzingmultiple frames to inspect an object, where “inspect” means to determinesome information about the status of the object. In one example of thismethod, the status of an object includes whether or not the objectsatisfies inspection criteria chosen as appropriate by a user.

In some aspects of the Vision Detector Method and Apparatus dynamicimage analysis is combined with visual event detection, so that theactive frames chosen by the visual event detection method are the onesused by the dynamic image analysis method to inspect the object. Inother aspects of the Vision Detector Method and Apparatus, the frames tobe used by dynamic image analysis can be captured in response to atrigger signal.

Each such frame is analyzed to determine evidence that the objectsatisfies the inspection criteria. In one exemplary method, the evidenceis in the form of a value, called an object pass score, that indicates alevel of confidence that the object satisfies the inspection criteria.As with object detection weights, the value may be a simple yes/nochoice that indicates high or low confidence, a number, such as a fuzzylogic value, that indicates a range of levels of confidence, or any itemof information that conveys evidence.

The status of the object may be determined from statistics of the objectpass scores, such as an average or percentile of the object pass scores.The status may also be determined by weighted statistics, such as aweighted average or weighted percentile, using the object detectionweights. Weighted statistics effectively weight evidence more heavilyfrom frames wherein the confidence is higher that the object is actuallylocated in the field of view for that frame.

Evidence for object detection and inspection is obtained by examining aframe for information about one or more visible features of the object.A visible feature is a portion of the object wherein the amount,pattern, or other characteristic of emitted light conveys informationabout the presence, identity, or status of the object. Light can beemitted by any process or combination of processes, including but notlimited to reflection, transmission, or refraction of a source externalor internal to the object, or directly from a source internal to theobject.

One aspect of the Vision Detector Method and Apparatus is a method forobtaining evidence, including object detection weights and object passscores, by image analysis operations on one or more regions of interestin each frame for which the evidence is needed. In an example of thismethod, the image analysis operation computes a measurement based on thepixel values in the region of interest, where the measurement isresponsive to some appropriate characteristic of a visible feature ofthe object. The measurement is converted to a logic value by a thresholdoperation, and the logic values obtained from the regions of interestare combined to produce the evidence for the frame. The logic values canbe binary or fuzzy logic values, with the thresholds and logicalcombination being binary or fuzzy as appropriate.

For visual event detection, evidence that an object is located in thefield of view is effectively defined by the regions of interest,measurements, thresholds, logical combinations, and other parametersfurther described herein, which are collectively called theconfiguration of the vision detector and are chosen by a user asappropriate for a given application of the invention. Similarly, theconfiguration of the vision detector defines what constitutes sufficientevidence.

For dynamic image analysis, evidence that an object satisfies theinspection criteria is also effectively defined by the configuration ofthe vision detector.

Discussion of the Problem

Given the limitations of photodetectors and machine vision systems inproviding triggers for high-speed event detection, motion analysis, andimage recording, there is a need for improved methods and systems thatavoid the need for a trigger signal by providing high-speed visual eventdetection and integrating it with high-speed image recording.

The Vision Detector Method and Apparatus teaches novel image analysismethods and systems that provide, among other benefits, high-speedvisual event detection, but without teaching any integration with imagerecording for use in motion analysis. Thus there is a need for improvedmethods and systems that combine suitable elements and configurations ofthe Vision Detector Method and Apparatus with suitable image recordingand display capabilities to achieve novel and useful methods and systemsfor automatic visual detection, recording, and retrieval of events.

Furthermore, the Vision Detector Method and Apparatus providesillustrative embodiments of visual event detection that are primarilyintended to detect events corresponding to discrete objects passingthrough the field of view. While it will be clear to one of ordinaryskill that these teachings may be used to detect other types of events,improvements not taught therein may also be useful in detecting suchevents. Thus there is a need to expand the teachings of visual eventdetection to improve its utility in detecting a variety of events.

SUMMARY OF THE INVENTION

The invention provides methods and systems for automatic visualdetection, recording, and retrieval of events. Herein

-   -   an “event” corresponds to a specific condition, among some        time-varying conditions within the field of view of an imaging        device, that can be detected by visual means;    -   “automatic visual detection” means that events are detected        without need for human intervention or external trigger signals,        based on the content of images captured by the imaging device;    -   “recording” means that images corresponding to times before,        during, and/or after the event are stored in a memory; and

“retrieval” means that these images can be retrieved for purposesincluding display for a human user and further automated analysis by animage analysis system.

The methods and systems taught herein are useful for automatic visualdetection of events for any purpose, including but not limited tosignaling external equipment that an event has occurred and providing asynchronized output pulse that indicates when the event occurred. Theyare further useful for high-speed motion analysis of a mechanicalprocess, and any other application for which images of short-duration,rare events are desired.

According to the teachings of the invention, a vision detector or othersuitable device is placed so that its field of view includes sometime-varying conditions, such as a mechanical process, wherein an eventcorresponding to some specific conditions may occur, and is configuredto detect the events. The vision detector captures a sequence of frames,where each frame is an image of the field of view, and analyzes theframes using any of a variety of methods and systems, including but notlimited to those taught in Vision Detector Method and Apparatus, andfurther detailed below, to obtain evidence that an event in the field ofview has occurred.

When an event occurs the analysis will identify a set of event framesthat together reveal sufficient evidence that the event has occurred.The set may contain just one event frame, and it may also contain aplurality of event frames. In the illustrative embodiments taught hereinthe event frames are consecutive in the sequence of frames, but it isstraightforward to devise embodiments within the scope of the inventionwhere the event frames are not strictly consecutive.

Consider the following example. The event to be detected corresponds toa moving mechanical component traveling outside a zone of acceptabletolerance. A vision detector is configured to detect the component in anerror zone, a region of the field of view that is outside the acceptablezone. Suppose that on some machine cycle the component moves through theerror zone for three consecutive frames. Suppose further that theanalysis of the frames reveals strong evidence that the component is inthe error zone for the first and third frames, but weak evidence thatthe component is in the error zone for the second frame. This may occurbecause the viewing perspective or position of the component relative tothe illumination in the second frame is such that the component isdifficult to see. The analysis also reveals that the component isunlikely to have been in the zone for many frames before and after thethree critical frames.

The set of event frames began when the first frame revealed strongevidence that the event was occurring, and ended at the third frame whensubsequent frames revealed no evidence that the event was continuing.The combined positive evidence of the first and third frames and weakevidence of the second frame is judged to be sufficient to conclude thatthe event has occurred. The three frames are the event frames in thisexample.

In some embodiments the second frame is not considered an eventframe—the choice of whether or not to consider the second frame to be anevent frame can be made either way within the scope of the invention. Inthe illustrative embodiments taught herein, the event frames areconsecutive and would include the second frame

When the evidence is judged to be sufficient to decide that an event hasoccurred, a plurality of selected frames are chosen from the sequence offrames to be recorded in a memory. A frame is chosen to be recordeddepending on its position in the sequence of frames relative either tothe event frames, or to a mark time computed as described herein. Theevent frames themselves may be recorded, frames prior to the eventframes in the sequence may be recorded, and frames after the eventframes in the sequence may be recorded. In an illustrative embodiment,frames captured within a user-specified time interval relative to themark time are recorded. In another illustrative embodiment apredetermined number of frames are recorded, including the event framesand consecutive frames immediately prior to and immediately after theevent frames.

Frames from these stored selected frames are retrieved in response tocommands and used for various purposes, including display for a humanuser who is using a graphical user interface to issue the commands, andfurther automated image analysis by an image analysis system that isissuing the commands.

In embodiments where the frames are displayed for a human user, it isgenerally desirable to display several frames at once but typically notpractical to display all of the recorded images at once at a displayresolution sufficient for the user to see useful detail in each image.According to the invention a portion of the recorded frames aredisplayed at one time. The user chooses the portion to be displayed byissuing scrolling commands to advance the portion to be displayedforward or backward. The portion to be displayed at one time preferablyincludes several frames, but can include as few as one frame.

In an illustrative embodiment, the frames are displayed using agraphical user interface (GUI). The portion of frames displayed at onetime are contained in a filmstrip window of the GUI, which displays theportion of frames as a succession of low-resolution “thumbnail” images.The resolution of the thumbnail images is chosen to be low enough that auseful number of images can been seen at one time, and high enough thateach image is sufficiently detailed to be useful. The scrolling commandsare provided by conventional GUI elements.

This illustrative embodiment further displays one frame of the portionof frames at full resolution in an image view window. As the scrollingcommands advance the filmstrip forward and/or backward, the framedisplayed in the image view window will also be advanced forward orbackward.

In an illustrative embodiment, evidence that an event in the field ofview has occurred is obtained for each frame in the form of a value,called an event detection weight, that indicates a level of confidencethat the event is occurring or has occurred. The value may be a simpleyes/no choice that indicates high or low confidence, a number thatindicates a range of levels of confidence, or any item of informationthat conveys evidence. One example of such a number is a so-called fuzzylogic value, further described herein. Note that no machine can make aperfect decision from an image, and so will instead make judgments basedon imperfect evidence.

An event detection weight is obtained for each frame by image analysisoperations on one or more regions of interest in the frame. In anillustrative embodiment, each image analysis operation computes ameasurement based on the pixel values in the region of interest, wherethe measurement is responsive to the amount, pattern, or othercharacteristic of light within the region. The measurement is convertedto a logic value by a threshold operation, and the logic values obtainedfrom the regions of interest are combined to produce the event detectionweight for the frame. The logic values can be binary or fuzzy logicvalues, with the thresholds and logical combination being binary orfuzzy as appropriate.

In an illustrative embodiment, event frames are a set of consecutiveframes for which:

-   -   the event detection weight for the first and last frames in the        set exceed a threshold;    -   the last frame is followed by at least some predetermined number        of frames where the event detection weight does not exceed the        threshold; and    -   the event detection weights of the set of frames satisfy some        predetermined condition.

Any suitable condition can be defined for this purpose, which can dependon statistics including but not limited to:

-   -   the number of frames in the set;    -   the average (mean) event detection weight;    -   the total event detection weight;    -   the median event detection weight; and    -   the number or fraction of frames in the set for which the event        detection weight exceeds a threshold.

It is desirable that frames recorded according to the present inventionbe stamped with the time at which they were captured. To supportdetailed study of the event, it is most useful for the timestamps to berelative to the time of occurrence of the event itself, rather than, forexample, time of day. The event frames define a broad range of times forthe event, however, not a specific point in time. In order to obtain aspecific and meaningful time for the event, one may use the mark time astaught in Vision Detector Method and Apparatus. As taught therein, marktime is the time at which an object crosses some fixed, imaginaryreference point, which can be computed accurately following thoseteachings.

Not all events correspond to an object crossing a reference point,however. In some cases, for example, the event may correspond to astroke motion, wherein a mechanical component advances and then retreatswithin the field of view. In such cases the mark time would moreusefully be defined as the apex of the stroke, rather than the crossingof a reference point. Motion across a reference point will be called aflow event, and motion of advance and retreat will be called a strokeevent. The present invention includes methods and systems for selectingbetween flow and stroke events, and computing mark time for strokeevents (mark time for flow events was taught in Vision Detector Methodand Apparatus).

Event detection according to the present invention is an example ofvisual event detection as taught in Vision Detector Method andApparatus, which states: “The method of visual event detection involvescapturing a sequence of frames and analyzing each frame to determineevidence that an event is occurring or has occurred.” As taught thereinvisual event detection was primarily directed towards embodiments wherethe events to be detected corresponded to discrete objects passingthrough the field of view, and where it was generally desirable toinspect those objects. The reader will note the strong similaritybetween object detection weights and object pass scores taught therein,and event detection weights used in the present invention. Indeed anymethod or apparatus taught therein for obtaining an object detectionweight or object pass score can be used to obtain an event detectionweight, the only difference being the purpose for which these weightsand scores are intended, and not the manner in which they are obtained.

Furthermore, detecting that an object has passed through the field ofview, or more particularly that a defective object has passed throughthe field of view, is an example of an event for which it may bedesirable to detect, record, and retrieve according to the presentinvention. Indeed there is little difference between detecting an objectpassing through the field of view and detecting a mechanical componententering an error zone as in the above example. Thus the teachings ofVision Detector Method and Apparatus may generally be consideredillustrative embodiments of the present invention, where recording andretrieval methods and systems, and improved event detection methods andsystems, would be added as taught herein.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention will be more fully understood from the following detaileddescription, in conjunction with the accompanying figures, wherein:

FIG. 1 shows an illustrative embodiment of a system for detecting,recording, and retrieving visual events according to the presentinvention, wherein the events correspond to applying defective labels onobjects moving on a production line;

FIG. 2 shows a timeline that illustrates a typical operating cycle for asystem detecting events according to the invention;

FIG. 3 shows a flowchart that describes analysis steps performed by anillustrative embodiment;

FIG. 4 illustrates how evidence is weighed for visual event detection inan illustrative embodiment;

FIG. 5 shows statistics that are gathered and used for event detectionin an illustrative embodiment;

FIG. 6 shows a high-level block diagram for a system according to theinvention;

FIG. 7 shows a block diagram of an illustrative embodiment of a visiondetector that can use used as part of a system according to theinvention;

FIG. 8 shows fuzzy logic elements used in an illustrative embodiment toweigh evidence and make judgments, including judging whether an objectis present and whether it passes inspection;

FIG. 9 shows the organization of a set of software elements (e.g.,program instructions of a computer readable medium) used by anillustrative embodiment to analyze frames, make judgments, sense inputs,and control output signals;

FIG. 10 shows a portion of an HMI for user configuration of eventdetection parameters, which will be used to further describe anillustrative embodiment of visual event detection;

FIG. 11 shows a portion of an exemplary configuration of a visiondetector that may be used to detect an improperly applied label on anexemplary object;

FIG. 12 shows another portion of the configuration corresponding to theexemplary setup of FIG. 11;

FIG. 13 shows a portion of an exemplary configuration of a visiondetector that may be used to detect an exemplary object that ismispositioned relative to a label application arm;

FIG. 14 shows another portion of the configuration corresponding to theexemplary setup of FIG. 13;

FIG. 15 shows a portion of an exemplary configuration of a visiondetector that may be used to detect a label application arm that under-or over-extends during a production cycle;

FIG. 16 shows how evidence is weighed to detect under- or over-extensionof the arm in the exemplary setup of FIG. 15;

FIG. 17 shows one way to configure the invention to detect eventscorresponding to flaws on a continuous web;

FIG. 18 shows a timing diagram that will be used to explain how outputsignals are synchronized to the time that an event occurs;

FIG. 19 shows how the mark time is computed for stroke events;

FIG. 20 shows a portion of the HMI for user configuration of outputsignals;

FIG. 21 shows a memory arrangement for recording images of detectedevents; and

FIG. 22 shows a portion of a graphical user interface containing afilmstrip window and an image view window, and showing images that havebeen recorded and retrieved corresponding to an event.

DETAILED DESCRIPTION OF THE INVENTION

Basic Operation of the Invention

FIG. 1 shows an illustrative embodiment of a vision detector configuredto detect certain events that may occur on a production line, and torecord and retrieve images of those events. A conveyer 100 transportsobjects, including example objects 110, 112, 114, 116, and 118, left toright past a labeling mechanism 160, which acts to place a label on eachobject, for example label 120 on object 112. The labeling mechanism 160includes an arm 162 that acts so as to move and apply each label to eachobject. Note that labeling mechanism 160 and arm 162 are shown forillustrative purposes to explain the invention, and do not necessarilyrepresent any particular mechanism used to apply labels in industrialproduction.

The labeling mechanism 160 may occasionally malfunction, resulting forexample in the misapplied label 122, whose lower right corner is bentaway from the surface of object 116. Many industrial productions lineswill use some form of automatic optoelectronic inspection, such as a setof photodetectors, a machine vision system, or a vision detector, todetect and reject defective object 116. While such inspection isvaluable in preventing defective objects from reaching customers, itdoes nothing to prevent defects from being made in the first place. Oneobjective of the present invention is to aid manufacturing engineers indiagnosing and fixing the cause of defective objects by providing imagesthat show the defect actually being created.

In addition to transporting objects for production purposes, conveyer100 causes relative movement between the objects and the field of a viewof vision detector 130. On many production lines motion of the conveyeris tracked by a shaft encoder 132, which produces a signal 140 that canbe received by vision detector 130 and used for various purposes astaught in Vision Detector Method and Apparatus and herein. For example,the signal 140 can be used by the vision detector 130 as a reference tothe point in time that object 112 crosses an imaginary reference point150, called the mark point.

Vision detector 130 detects certain events that may occur in its fieldof view, based on appropriate visual criteria. In the illustrativeembodiment of FIG. 1, an event would correspond to the misapplication ofa label by arm 162, which can be detected in various ways as furtherdescribed below. Images are recorded at times before, during, and afterthe event. A human user, such as a manufacturing engineer or technician,would interact with a Human-Machine Interface 134 via signal 142 toretrieve the recorded images so that the mechanical problems leading tomisapplied labels could be diagnosed.

In another embodiment there are no discrete objects, but rather materialflows past the vision detector continuously, for example on a web, anexample of which is provided below. For purposes of event detection,recording, and retrieval, there is little difference between discreteobjects and continuous flow of material.

FIG. 2 shows a timeline that illustrates a typical operating cycle for avision detector configured to detect events and record images. Boxeslabeled “c”, such as box 220, represent image capture. Boxes labeled“a”, such as box 230, represent analysis (analysis steps are furthersubdivided and described below). It is desirable that capture “c” of thenext image be overlapped with analysis “a” of the current image, so that(for example) analysis step 230 analyzes the image captured in capturestep 220. In this timeline, analysis is shown as taking less time thancapture, but in general analysis will be shorter or longer than capturedepending on the application details.

If capture and analysis are overlapped, the rate at which a visiondetector can capture and analyze images is determined by the longer ofthe capture time and the analysis time. This is the “frame rate”.

A portion 200 of the timeline corresponds to a first event, and includesthe capture and analysis of five event frames. A second portion 202corresponds to a second event, and includes three event frames.

In illustrative embodiments considered herein, analysis of the capturedimages for event detection includes three principal subdivisions:

-   -   a visual analysis step to evaluate evidence that an event is        occurring or has occurred in an individual frame;    -   an activity analysis step to identify a set of consecutive        frames, called candidate frames, for which an event to be        detected may be occurring; and    -   an event analysis step to determine whether or not an event to        be detected has occurred in a set of candidate frames.

Each visual analysis step considers the evidence that an event isoccurring in an individual frame. Frames where the evidence is strongare called active. Analysis steps for active frames are shown with athick border, for example analysis step 240. In illustrative embodimentsconsidered herein, this evidence is represented by a fuzzy logic valuecalled an event detection weight (further described below), which iscomputed by image analysis operations of one or more regions of interestin the frame as taught below and in Vision Detector Method andApparatus.

Each activity analysis step considers the evidence that an event isoccurring or has occurred within a recent set of frames. In illustrativeembodiments considered herein, event detection is considered to be ineither an active state, signifying that there is some evidence that anevent is occurring, or an inactive state, indicating that there islittle such evidence. One function of the activity step is to determinethis state. In an illustrative embodiment, a transition from theinactive state to the active state is made when an active frame isfound. A transition from the active state to the inactive state is madewhen some number of consecutive inactive frames are found. The candidateframes include the consecutive set of frames beginning with the firstactive frame and ending with the last active frame, and may includeinactive frames in between. Another function of the activity analysissteps is to gather statistics describing the set of candidate frames.

Each event analysis step then considers evidence that an event hasoccurred during the candidate frames by examining the evidence gatheredby preceding activity analysis steps. In illustrative embodimentsconsidered herein, an event analysis step is performed whenever atransition from the active state to the inactive state is made. Thestatistics describing the candidate frames are examined, and if theyreveal sufficient evidence to conclude that an event has occurred, thecandidate frames are considered event frames. Recording and otherappropriate actions as further described below are performed.

A variety of methods may be used perform visual, activity, and eventanalysis within the scope of the invention; some are described below andin Vision Detector Method and Apparatus, and many others will occur tothose skilled in the art.

In the example of FIG. 2, event detection for first event 200 enters theactive state with the first active frame corresponding to analysis step240, and ends with two consecutive inactive frames, corresponding toanalysis steps 246 and 248. Note that for the first event, a singleinactive frame corresponding to analysis step 242 is not sufficient toenter the inactive state.

When the inactive state is entered, for example at the end of analysisstep 248, an event analysis step is performed. The candidate frames arethe five event frames starting with analysis step 240 and ending withanalysis step 241. In this example the event analysis step concludesthat an event has occurred, and causes the recording of a first set ofrecorded frames 210. A similar analysis for second event 202 results inrecording of a second set of recorded frames 212.

By considering the position of the object in the active frames as itpasses through the field of view, as further described below, the visiondetector estimates mark times 250 and 252, which represent precise timesat which the events have occurred. A timestamp is stored for eachrecorded frame indicating the relative time between the mark time andthe midpoint of the shutter time corresponding to each such recordedframe.

Once a transition to the inactive state is made, the vision detector mayenter an idle step, for example first idle step 260 and second idle step262. Such a step is optional, but may be desirable for several reasons.If a minimum time between events is known, there is no need to belooking for an event until just before a new one might happen. An idlestep will eliminate the chance of false event detection at times when anevent couldn't happen, and will extend the lifetime of the illuminationsystem because the lights can be kept off during the idle step.

FIG. 3 shows a flowchart that provides details of the analysis steps ofevent detection. Boxes with a dashed border represent data used by theflowchart. Rounded rectangles enclosing flowchart blocks show theanalysis subdivisions, including visual analysis step 310, activityanalysis step 312, and event analysis step 314.

Active flag 300 holds the active/inactive state used by the activityanalysis steps.

Statistics gathered by the activity analysis steps are held in activestatistics 302 and inactive statistics 304. Values for active frames areadded directly to active statistics 302. For an inactive frame, theactivity analysis step does not yet know whether the frame will be partof the set of candidate frames—that depends on the status of futureframes. Thus values for inactive frames are added to inactive statistics304. If an active frame is subsequently found before a transition to theinactive state is made, inactive statistics 304 are added to activestatistics 302 and cleared. Inactive statistics 304 remaining at thetime of a transition to the inactive state are discarded. Bothstatistics include a count of the number of frames that have been added.

In the illustrative embodiment of FIG. 3, the flowchart is executedrepeatedly, once for each frame, from capture block 320 through continueblock 322. Capture block 320 synchronizes the analysis with the framecapture and provides for capture of the next frame to be overlapped withanalysis of the current frame. Visual analysis block 322 performs thevisual analysis step, computing an event detection weight d from ananalysis of the captured image.

Activity analysis step 312 is performed next. Active block 330 testsactive flag 300 to determine the current state of event detection. Ifevent detection is inactive, first threshold block 340 determines theactive/inactive status of the current frame by comparing d to athreshold t_(d). If the event detection weight d is not greater than thethreshold t_(d), the frame is inactive, and activity analysis ends forthe current frame. If the event detection weight d is greater than thethreshold t_(d), the frame is active, active transition block 342 setsactive flag 300, and initialize block 344 initializes active statistics302 using values from the current frame, and clears inactive statistics304.

If active block 330 determines that the current state is active, thensecond threshold block 346 determines the active/inactive status of thecurrent frame by comparing d to a threshold t_(d). If d is not greaterthan the threshold, then the frame is inactive, and count test block 350looks at the frame count in inactive statistics 304 to determine whethermore than a parameter k of consecutive inactive frames have been found.If not, inactive update block 354 updates inactive statistics 304 byadding values from the current frame (including incrementing the framecount). If so, inactive transition block 352 clears active flag 300 andexecution continues with event analysis step 314.

If second threshold block 346 determines that the current frame isactive, then active update block 360 updates active statistics 302 byadding values from both the current frame and inactive statistics 304.Clear block 362 then clears inactive statistics 304

If activity analysis step 312 makes a transition from the active stateto the inactive state, then event analysis step 314 is performed.Condition block 370 tests active statistics 302 to determine whether anevent has occurred. If not, gather statistics are ignored and executioncontinues. If so, event block 372 marks frames for recording, as furtherdescribed below. Idle block 374 waits for an idle interval beforecontinuing.

FIG. 4 further illustrates the analysis steps of an illustrativeembodiment, and can be used in conjunction with the timeline of FIG. 2and the flowchart of FIG. 3 to understand the basic operation of theinvention.

FIG. 4 illustrates how evidence is weighed for event detection in anillustrative embodiment. As discussed above, information comprisingevidence that an event is occurring or has occurred in the field of viewis called an event detection weight. The figure shows a plot of eventdetection weights d_(i) on vertical axis 400 versus frame count i onhorizontal axis 402. Each frame is represented by a vertical line, suchas example line 426. Note that the frame count is an arbitrary integer.

In this embodiment d_(i) is a fuzzy logic value representing evidencethat an event is occurring in frame i, and is computed by the visiondetector on each frame using methods further described below and inVision Detector Method and Apparatus.

In the illustrative embodiment of FIG. 4, event detection thresholdt_(d) is 0.5, so that frames where d_(i)≧0.5 are considered activeframes. For reference, a line 430 where d_(i)=0.5 is plotted. Eventdetection weights for active frames are plotted as solid circles, forexample point 410, and those for inactive frames are plotted as opencircles, for example point 416.

In the example of FIG. 4, event detection enters the active state onframe 422, and enters the inactive state after frame 424, when fourconsecutive inactive frames have been seen (inactive frame countthreshold k=3 in this example). The set of candidate frames starts withframe 422 and ends with frame 426. The isolated inactive frame 420 doesnot cause a transition to the inactive state.

FIG. 5 gives details of statistics gathered by activity analysis step312 and used by event analysis step 314 in an illustrative embodiment,and also for the example of FIG. 4. For this embodiment, activestatistics 302 and inactive statistics 304 would include sufficientinformation to compute these statistics when a transition to theinactive state is made. Each statistic in FIG. 5 includes a symbol 500,further described below, a description 510 that serves to define thevalue, and an example 520 that shows what the value would be for theexample of FIG. 4.

The above descriptions of methods for weighing evidence to determinewhether an event has been detected are intended as examples of usefulembodiments, but do not limit the methods that can be used within thescope of the invention. For example, the exemplary constants t_(d)=0.5used above may be replaced with any suitable value. Many additionalmethods for visual event detection will occur to those skilled in theart.

Illustrative Apparatus

FIG. 6 shows a high-level block diagram for a vision detector used forvisual detection, recording, and retrieval of events. A vision detector600 may be connected to appropriate automation equipment 610, which mayinclude PLCs, reject actuators, shaft encoders, and/or photodetectors,by means of signals 620. These connections are not required fordetection, recording, and retrieval of events, but may be useful incases where it is desirable to use the vision detector for additionalpurposes, for example those taught in Vision Detector Method andApparatus. It may be particularly desirable, for example, to provide anoutput pulse to signal that an event has been detected. Such as pulsewould be delayed and synchronized to a mark time as taught in VisionDetector Method and Apparatus, and used by a PLC, actuator, or otherdevice.

For retrieval display of images of detected events, the vision detectoris connected to a human-machine interface (HMI) 630, such as a PC orhand-held device, by means of signals 640. HMI 630 is also used forsetup. HMI 630 need not be connected for detection and recording ofevents, but must of course be reconnected for retrieval and display.Signals 640 can be implemented in any acceptable format and/or protocoland transmitted in a wired or wireless form.

Images of events recorded by vision detector 600 may also be retrievedby an automated image analysis system 650, including but not limited toa conventional machine vision system. Such a system might be used tomake a more sophisticated analysis of the images than might be possiblewith a vision detector designed to operate at very high frame rates, butwithout requiring the human analysis inherent in the use of HMI 630.

FIG. 7 shows a block diagram of an illustrative embodiment of a visiondetector that might be used to practice the invention. A digital signalprocessor (DSP) 700 runs software to control capture, analysis,recording, HMI communications, and any other appropriate functionsneeded by the vision detector. The DSP 700 is interfaced to a memory710, which includes high speed random access memory for programs anddata and non-volatile memory to hold programs and setup information whenpower is removed. The memory 710 also holds recorded images forsubsequent retrieval. The DSP is also connected to an I/O module 720that provides signals to automation equipment, an HMI interface 730, anillumination module 740, and an imager 760. A lens 750 focuses imagesonto the photosensitive elements of the imager 760.

The DSP 700 can be any device capable of digital computation,information storage, and interface to other digital elements, includingbut not limited to a general-purpose computer, a PLC, or amicroprocessor. It is desirable that the DSP 700 be inexpensive but fastenough to handle a high frame rate. It is further desirable that it becapable of receiving and storing pixel data from the imagersimultaneously with image analysis.

In the illustrative embodiment of FIG. 7, the DSP 700 is an ADSP-BF531manufactured by Analog Devices of Norwood, Mass. The Parallel PeripheralInterface (PPI) 770 of the ADSP-BF531 DSP 700 receives pixel data fromthe imager 760, and sends the data to memory controller 774 via DirectMemory Access (DMA) channel 772 for storage in memory 710. The use ofthe PPI 770 and DMA 772 allows, under appropriate software control,image capture to occur simultaneously with any other analysis performedby the DSP 700. Software instructions to control the PPI 770 and DMA 772can be implemented by one of ordinary skill in the art following theprogramming instructions contained in the ADSP-BF533 Blackfin ProcessorHardware Reference (part number 82-002005-01), and the BlackfinProcessor Instruction Set Reference (part number 82-000410-14), bothincorporated herein by reference. Note that the ADSP-BF531, and thecompatible ADSP-BF532 and ADSP-BF533 devices, have identical programminginstructions and can be used interchangeably in this illustrativeembodiment to obtain an appropriate price/performance tradeoff.

The high frame rate desired by a vision detector suggests the use of animager unlike those that have been used in prior art vision systems. Itis desirable that the imager be unusually light sensitive, so that itcan operate with extremely short shutter times using inexpensiveillumination. It is further desirable that it be able to digitize andtransmit pixel data to the DSP far faster than prior art vision systems.It is moreover desirable that it be inexpensive and have a globalshutter.

These objectives may be met by choosing an imager with much higher lightsensitivity and lower resolution than those used by prior art visionsystems. In the illustrative embodiment of FIG. 7, the imager 760 is anLM9630 manufactured by National Semiconductor of Santa Clara, Calif. TheLM9630 has an array of 128 by 100 pixels, for a total of 12800, about 24times fewer than typical prior art vision systems. The pixels arerelatively large at 20 microns square, providing high light sensitivity.The LM9630 can provide 500 frames per second when set for a 300microsecond shutter time, and is sensitive enough (in most cases) toallow a 300 microsecond shutter using LED illumination. This resolutionwould be considered far too low for a vision system, but is quitesufficient for the feature detection tasks that are the objectives ofthe Vision Detector Method and Apparatus. Electrical interface andsoftware control of the LM9630 can be implemented by one of ordinaryskill in the art following the instructions contained in the LM9630 DataSheet, Rev 1.0, January 2004, which is incorporated herein by reference.

It is desirable that the illumination 740 be inexpensive and yet brightenough to allow short shutter times. In an illustrative embodiment, abank of high-intensity red LEDs operating at 630 nanometers is used, forexample the HLMP-ED25 manufactured by Agilent Technologies. In anotherembodiment, high-intensity white LEDs are used to implement desiredillumination.

In the illustrative embodiment of FIG. 7, the I/O module 720 providesoutput signals 722 and 724, and input signal 726. Input signal 726 canbe used for event detection by appropriate connections in a logic viewas taught in Vision Detector Method and Apparatus.

As used herein an image capture device provides means to capture andstore a digital image. In the illustrative embodiment of FIG. 7, imagecapture device 780 comprises a DSP 700, imager 760, memory 710, andassociated electrical interfaces and software instructions.

As used herein an analyzer provides means for analysis of digital data,including but not limited to a digital image. In the illustrativeembodiment of FIG. 7, analyzer 782 comprises a DSP 700, a memory 710,and associated electrical interfaces and software instructions.

As used herein an output signaler provides means to produce an outputsignal responsive to an analysis. In the illustrative embodiment of FIG.7, output signaler 784 comprises an I/O module 720 and an output signal722.

As used herein a process refers to systematic set of actions directed tosome purpose, carried out by any suitable apparatus, including but notlimited to a mechanism, device, component, software, or firmware, or anycombination thereof that work together in one location or a variety oflocations to carry out the intended actions.

In an illustrative embodiment, various processes used by the presentinvention are carried out by an interacting collection of digitalhardware elements and computer software instructions. These hardwareelements include

-   -   DSP 700, which provides general-purpose information processing        actions under control of suitable computer software        instructions;    -   memory 710, which provides storage and retrieval actions for        images, data, and computer software instructions;    -   imager 760, which provides, in combination with other elements        as described herein, image capture actions;    -   I/O module 720, which provides interface and signaling actions;        and    -   HMI interface 730, which provides human-machine interface        actions.

In an illustrative embodiment the computer software instructions includethose for carrying out the actions described herein for

-   -   the flowchart steps of FIG. 3, which describes portions of        illustrative capture and analysis processes;    -   the fuzzy logic elements of FIG. 8, which describes illustrative        decision logic;    -   the software elements of FIG. 9, which illustrates a set of        software elements that can be used to practice the invention;        and    -   the graphical controls of FIGS. 10 and 20, which illustrate how        human users can select operating parameters.

Furthermore, it will be understood by those skilled in the art that theabove is a list of examples only. It is not exhaustive, and suitablecomputer software instructions may be used in illustrative embodimentsto carry out processes used for any figure described herein.

Examples of processes described herein include:

-   -   a capture process, comprising capture block 320 (FIG. 3), and        other actions as described herein, and carried out by image        capture device 780;    -   a variety of analysis processes, comprising portions of FIG. 3,        for example visual analysis step 310, activity analysis step        312, and event analysis step 314, and other actions as described        herein, and carried out by analyzer 782 and suitable software        elements shown in FIG. 9;    -   a variety of selection processes, comprising for example event        block 372, and other actions as described herein, and carried        out by analyzer 782 and suitable software elements shown in FIG.        9; and    -   a variety of decision processes, comprising for example        condition block 370, and other actions as described herein, and        carried out by analyzer 782 and suitable software elements shown        in FIG. 9.

It will be understood by one of ordinary skill that there are manyalternate arrangements, devices, and software instructions that could beused within the scope of the invention to implement an image capturedevice 780, analyzer 782, and output signaler 784. Similarly, manyalternate arrangements, devices, and software instructions could be usedwithin the scope of the invention to carry out the processes describedherein.

Fuzzy Logic Decision Making

FIG. 8 shows fuzzy logic elements used in an illustrative embodiment toweigh evidence and make judgments, including judging whether an event isoccurring or has occurred.

A fuzzy logic value is a number between 0 and 1 that represents anestimate of confidence that some specific condition is true. A value of1 signifies high confidence that the condition is true, 0 signifies highconfidence that the condition is false, and intermediate values signifyintermediate levels of confidence.

The more familiar binary logic is a subset of fuzzy logic, where theconfidence values are restricted to just 0 and 1. Therefore, anyembodiment described herein that uses fuzzy logic values can use as analternative binary logic values, with any fuzzy logic method orapparatus using those values replaced with an equivalent binary logicmethod or apparatus.

Just as binary logic values are obtained from raw measurements by usinga threshold, fuzzy logic values are obtained using a fuzzy threshold.Referring to FIG. 8, a graph 800 illustrates a fuzzy threshold. Thex-axis 810 represents a raw measurement, and the f-axis 814 representsthe fuzzy logic value, which is a function whose domain includes allpossible raw measurements and whose range is 0≦f≦1.

In an illustrative embodiment, a fuzzy threshold comprises two numbersshown on the x-axis, low threshold t₀ 820, and high threshold t₁ 822,corresponding to points on the function 824 and 826. The fuzzy thresholdcan be defined by the equation $\begin{matrix}{f = {\min\left( {{\max\left( {\frac{x - t_{0}}{t_{1} - t_{0}},0} \right)},1} \right)}} & (1)\end{matrix}$

Note that this function works just as well when t₁<t₀. Other functionscan also be used for a fuzzy threshold, such as the sigmoid$\begin{matrix}{f = \frac{1}{1 + {\mathbb{e}}^{{- {({x - t})}}/\sigma}}} & (2)\end{matrix}$where t and σ are threshold parameters. In embodiments where simplicityis a goal, a conventional binary threshold can be used, resulting inbinary logic values.

Fuzzy decision making is based on fuzzy versions of AND 840, OR 850, andNOT 860. A fuzzy AND of two or more fuzzy logic values is the minimumvalue, and a fuzzy OR is the maximum value. Fuzzy NOT off is 1−f Fuzzylogic is identical to binary when the fuzzy logic values are restrictedto 0 and 1.

In an illustrative embodiment, whenever a hard true/false decision isneeded, a fuzzy logic value is considered true if it is at least 0.5,false if it is less than 0.5.

It will be clear to one skilled in the art that there is nothingcritical about the values 0 and 1 as used in connection with fuzzy logicherein. Any number could be used to represent high confidence that acondition is true, and any different number could be used to representhigh confidence that the condition is false, with intermediate valuesrepresenting intermediate levels of confidence.

Software Elements of the Invention

FIG. 9 shows the organization of a set of software elements (e.g.,program instructions of a computer readable medium) used by anillustrative embodiment to analyze frames, make judgments, sense inputs,and control output signals. The elements may be implemented using aclass hierarchy in a conventional object-oriented programming languagesuch as C++, so that each of the elements corresponds to a class.However, any acceptable programming technique and/or language can beused to carry out the processes described herein.

As illustrated, classes with a dotted border, such as Gadget class 900,are abstract base classes that do not exist by themselves but are usedto build concrete derived classes such as Locator class 920. Classeswith a solid border represent dynamic objects that can be created anddestroyed as needed by the user in setting up an application, using anHMI 630. Classes with a dashed border, such as Input class 950,represent static objects associated with specific hardware or softwareresources. Static objects always exist and cannot be created ordestroyed by the user.

All classes are derived from Gadget class 900, and so all objects thatare instances of the classes shown in FIG. 9 are a kind of Gadget. In anillustrative embodiment, every Gadget:

-   -   1. has a name that can be chosen by the user;    -   2. has a logic output (a fuzzy logic value) that can be used as        a logic input by other gadgets to make judgments and control        output signals;    -   3. has a set of parameters than can be configured by a user to        specify its operation;    -   4. has one such parameter that can be used to invert the logic        output (i.e. fuzzy NOT); and    -   5. can be run, which causes its logic output to be updated based        on its parameters, logic inputs if any, and for certain Gadgets        the contents of the current frame, and which may also cause        side-effects such as the setting of an output signal.

The act of analyzing a frame consists of running each Gadget once, in anorder determined to guarantee that all logic inputs to a Gadget havebeen updated before the Gadget is run. In some embodiments, a Gadget isnot run during a frame where its logic output is not needed.

The Photo class 910 is the base class for all Gadgets whose logic outputdepends on the contents of the current frame. These are the classes thatactually do the image analysis. Every Photo measures some characteristicof a region of interest (ROI) of the current frame. The ROI correspondsto a visible feature on the object to be inspected. This measurement iscalled the Photo's analog output. The Photo's logic output is computedfrom the analog output by means of a fuzzy threshold, called thesensitivity threshold, that is among its set of parameters that can beconfigured by a user. The logic output of a Photo can be used to provideevidence to be used in making judgments.

The Detector class 930 is the base class for Photos whose primarypurpose is to make measurements in an ROI and provide evidence to beused in making judgments. In an illustrative embodiment all DetectorROIs are circles. A circular ROI simplifies the implementation becausethere is no need to deal with rotation, and having only one ROI shapesimplifies what the user has to learn. Detector parameters include theposition and diameter of the ROI.

A Brightness Detector 940 measures a weighted average or percentilebrightness in the ROI. A Contrast Detector 942 measures contrast in theROI. An Edge Detector 944 measures the extent to which the ROI lookslike an edge in a specific direction. A Spot Detector 946 measures theextent to which the ROI looks like a round feature such as a hole. ATemplate Detector 948 measures the extent to which the ROI looks like apre-trained pattern selected by a user. The operation of the Detectorsis further described in Vision Detector Method and Apparatus.

The Locator class 920 represents Photos that have two primary purposes.The first is to produce a logic output that can provide evidence formaking judgments, and in this they can be used like any Detector. Thesecond is to determine the location of an object in the field of view ofa vision detector, so that the position of the ROI of other Photos canbe moved so as to track the position of the object. Any Locator can beused for either or both purposes.

In an illustrative embodiment, a Locator searches a one-dimensionalrange in a frame for an edge. The search direction is normal to theedge, and is among the parameters to be configured by the user. Theanalog output of a Locator is similar to that for an Edge Detector.Locators are further described in Vision Detector Method and Apparatus.

In other embodiments, a Locator searches a multi-dimensional searchrange, using well-known methods, that may include translation, rotation,and size degrees of freedom. Suitable methods include those based onnormalized correlation, the generalized Hough transform, and geometricpattern patching, all of which are well-known in the art and have beencommercially available for many years. An illustrative embodiment of amulti-dimensional locator is provided in co-pending U.S. patentapplication Ser. No. 10/979,535, entitled METHOD FOR SETTING PARAMETERSOF A VISION DETECTOR USING PRODUCTION LINE INFORMATION, by Brian Mirtichand William M. Silver, filed Nov. 2, 2004, the teachings of which areexpressly incorporated herein by reference

The Input class 950 represents input signals to the vision detector,which can be used to influence event detection. The Output class 952represents output signals from the vision detector, such as might beused to inform a PLC or actuator that an event has been detected. In theillustrative embodiment there is one static instance of the Input classfor each physical input, such as exemplary input signal 726 (FIG. 7),and one static instance of the Output class for each physical output,such as exemplary output signals 722 and 724. An Output can producedelayed pulses synchronized to the mark time, as taught in VisionDetector Method and Apparatus, so that external automation equipment candetermine when, using delay times, or where, using encoder counts, theevent occurred.

The Gate base class 960 implements fuzzy logic decision making. EachGate has one or more logic inputs than can be connected to the logicoutputs of other Gadgets. Each logic input can be inverted (fuzzy NOT)by means of a parameter that a user can configure. An AND Gate 962implements a fuzzy AND operation, and an OR Gate 964 implements a fuzzyOR operation.

The Judge class 970 is the base class for objects that weigh evidenceover successive frames to make decisions. An illustrative embodiment ofthe present invention includes the EventDetect Judge 972, whose purposeis to implement activity analysis step 312 and event analysis step 314(visual analysis step 310 is performed by some combination of Photos,Inputs, and/or Gates, with examples given below). Other types of Judgesare taught in Vision Detector Method and Apparatus, and by be present inembodiment where it is desirable to combine functions provided thereinwith event detection as provided herein.

Each Judge has a logic input to which a user connects the logic outputof a Photo or, more typically, a Gate that provides a logicalcombination of Gadgets, usually Photos and other Gates. The logic inputto the EventDetect Judge provides the event detection weight for eachframe. It is expressly contemplated that embodiments of the inventionmay use more than one EventDetect Judge, an example of which will begiven below.

The logic output of the EventDetect Judge provides a pulse thatindicates when an event has been detected. The leading edge of the pulseoccurs when event analysis step 314 detects an event, for example at theend of analysis step 248 in FIG. 2, and the trailing edge occurs sometime after that, for example at the end of idle step 260.

FIG. 10 shows graphical controls that can be displayed on an HMI for auser to view and manipulate in order to set parameters for anEventDetect Judge. A set of graphical controls displayed on HMI 630 forsetting Gadget parameters is called a parameter view.

Name text box 1000 allows a user to view and enter a name for thisEventDetect Judge. Time label 1002 shows the time taken by the mostrecent run of this EventDetect Judge. Logic output label 1004 shows thecurrent logic output value of this EventDetect Judge, and may changecolor, shape, or other characteristic to distinguish between true (≧0.5)and false (<0.5). Invert checkbox 1006 allows the logic output of thisEventDetect Judge to be inverted. Note that name text box 1000, timelabel 1002, logic output label 1004, and invert checkbox 1006 are commonto the parameter views for all Gadget types, as further explained inVision Detector Method and Apparatus.

Idle time spinner 1020 allows a user to specify the time interval foridle step 260 (FIG. 2), also shown as idle block 374 in FIG. 3.

Missing frame spinner 1030 allows a user to specify the maximum numberof consecutive inactive frames that will be accepted without activityanalysis step 312 making a transition to the inactive state. The valuespecified by missing frame spinner 1030 is used for the parameter k incount test block 350 of FIG. 3.

Marking control 1040 allows a user to select between flow and strokeevents for computing mark time, as further described herein. To computemark time the user must specify a Locator using locator list control1042.

Recording interval controls 1050 allow the user to specify the timeinterval within which images are recorded when an event is detected,relative to the mark time. In an alternate embodiment, not shown, theuser specifies whether or not to record the event frames, and the numberof frames before and after the event frames to record.

Condition text 1010 allows the user to specify the event conditiontested by condition block 370, and used by event analysis step 314 todetermine whether an event has occurred. In the illustrative embodimentof FIG. 10, condition text 1010 contains a text string representing alogical expression in a syntax similar to that used by conventionalprogramming languages such as C. The expression combines symbols 500from FIG. 5, representing elements of active statistics 302, withnumeric constants, logical, comparison, and arithmetic operators, andpunctuation such as parenthesis, to specify the computation of atrue/false value from active statistics 302. Methods for computing atrue/false value based on such a text string are well-known in the art.

In the illustrative example of FIG. 10, an event has occurred if thereare at least two candidate frames and the mean event detection weight iseither less than 0.50 or greater than 0.75. An example where such acondition would be useful is shown in FIGS. 15 and 16, described below.

Examples of Use of Illustrative Embodiments

FIG. 11 shows an example of how Photos can be used to detect an eventcorresponding to an object with a misapplied label, such as misappliedlabel 122 (FIG. 1). FIG. 11 represents an image of an object 1100, whichmight correspond to object 116 from FIG. 1, containing label 1110, withsuperimposed graphics representing the Photos, and is displayed on anHMI 630 for a user to view and manipulate. A display of an image andsuperimposed graphics on an HMI is called an image view.

A Locator 1120 is used to detect and locate the top edge of the object,and another Locator 1122 is used to detect and locate the right edge. ABrightness Detector 1130 is used to help detect the presence of theobject. In this example the background is brighter than the object, andthe sensitivity threshold is set to distinguish the two brightnesslevels, with the logic output inverted to detect the darker object andnot the brighter background.

Together the Locators 1120 and 1122, and the Brightness Detector 1130,provide the evidence needed to judge that an object is present, asfurther described below. Clearly, an event corresponding to “object withmisapplied label” cannot occur unless an object is present.

An Edge Detector 1160 is used to detect the presence and position of thelabel 1110. If the label is absent, mis-positioned horizontally,significantly rotated, has a bent corner as shown, or is misapplied invarious other ways, the analog output of the Edge Detector would be verylow. Of course there are many ways that label 1110 could be misappliedthat would not be detected by Edge Detector 1160, and so other Photosmight be used as needed to detect failures in any given productionsituation.

For example, a Brightness Detector 1150 is used to verify that thecorrect label has been applied. In this example, the correct label iswhite and incorrect labels are darker colors.

As the object moves from left to right through the field of view of thevision detector, Locator 1122 tracks the right edge of the object andrepositions Brightness Detector 1130, Brightness Detector 1150, and EdgeDetector 1160 to be at the correct position relative to the object.Locator 1120 corrects for any variation in the vertical position of theobject in the field of view, repositioning the detectors based on thelocation of the top edge of the object. In general Locators can beoriented in any direction.

A user can manipulate Photos in an image view by using well-known HMItechniques. A Photo can be selected by clicking with a mouse, and itsROI can be moved, resized, and rotated by dragging. Additionalmanipulations for Locators are described in Vision Detector Method andApparatus.

FIG. 12 shows a logic view containing a wiring diagram corresponding tothe example setup of FIG. 11. A wiring diagram shows all Gadgets beingused to detect events and interface to automation equipment, and theconnections between logic inputs and outputs of the Gadgets. A wiringdiagram is displayed on an HMI 630 for a user to view and manipulate. Adisplay of gadgets and their logic interconnections on an HMI is calleda logic view.

Referring still to the wiring diagram of FIG. 12, a Locator 1220 named“Top”, corresponding to Locator 1120 in the image view of FIG. 11, isconnected to AND Gate 1210 by wire 1224. Similarly, “Side” Locator 1222,corresponding to Locator 1122, and “Box” Detector 1230, corresponding toBrightness Detector 1130, are also wired to AND Gate 1210. The logicoutput of “Box” detector 1230 is inverted, as shown by the small circle1232 and as described above to detect the darker object against alighter background.

In the wiring diagram, Brightness Detector “Label” 1250, correspondingto Brightness Detector 1150, and Edge Detector “LabelEdge” 1260,corresponding to Edge Detector 1160, are wired to AND Gate 1212. Thelogic output of AND Gate 1212 is inverted to represent the level ofconfidence that label 1210 is misapplied, and is wired to AND Gate 1210.

The logic output of AND Gate 1210 represents the level of confidencethat an object is present and its label has been misapplied, i.e. thelevel of confidence that an event has occurred. The logic output of ANDGate 1210 is wired to EventDetect Judge 1200 to be used as the eventdetection weight for each frame. An event condition for EventDetectJudge 1200 suitable for this configuration would be “n>=3 & m>=0.5”,although many alternate event conditions would also be suitabledepending on the circumstances of the application.

The choice of Gadgets to wire to an EventDetect Judge is made by a userbased on knowledge of the application. In the example of FIGS. 11 and12, a user may have determined that detecting just the top and rightedges was not sufficient to insure that an object is present. Note thatLocator 1122 might respond to the label's left edge just as strongly asthe object's right edge, and perhaps at this point in the productioncycle Locator 1120 might occasionally find some other edge in thebackground. By adding Detector 1130, and requiring all three conditionsby means of AND Gate 1210, event detection is made reliable.

When an event is detected, images may be recorded as further describedbelow. Clearly, images corresponding to times prior to the event frameswould be most likely to show exactly how the label was misapplied. It isobviously desirable in this case that the vision detector be placed tobe able to see the object as close as is practical to the place wherethe label is applied.

The logic output of EventDetect Judge 1200 is wired to an Output gadget1280, named “Signal”, which controls an output signal from the visiondetector than can if desired be connected to automation equipment suchas a PLC or actuator. The Output Gadget 1280 is configured by a user asappropriate, as further described in Vision Detector Method andApparatus. Output Gadget 1280 can produce delayed pulses synchronized tothe mark time, as taught in Vision Detector Method and Apparatus, sothat the automation equipment can determine when, using time, or where,using encoder count, the event occurred.

A user can manipulate Gadgets in a logic view by using well-known HMItechniques. A Gadget can be selected by clicking with a mouse, itsposition can be moved by dragging, and wires can be created by adrag-drop operation.

One skilled in the art will recognize that a wide variety of events canbe detected by suitable choice, configuration, and wiring of Gadgets.One skilled in the art will also recognize that the Gadget classhierarchy is only one of many software techniques that could be used topractice the invention.

FIG. 13 shows an image view corresponding to another configuration of avision detector to detect an event that might be useful for theproduction setup shown in FIG. 1. In this example, the event occurs whenlabeling arm 162 is fully extended (at the apex of its stroke) butobject 1300 is at the wrong position to receive label 1310.

Arm Edge Detector 1340 is placed at a position within the field of viewcorresponding to the apex of the stroke of labeling arm 162. Note thatthis position is fixed relative to the field of view—it does not movewith the production line, and so there is no need to employ a Locator.Top Edge Detector 1320 and side Edge Detector 1330 are used to verifythat object 1300 is in the desired position at the apex of the stroke.

FIG. 14 is a logic view showing a configuration of Gadgets correspondingto the image view of FIG. 13, for detecting an event corresponding to anobject in the wrong position at the apex of the stroke of the labelingarm 162. “Arm” 1440 corresponds to Arm Edge Detector 1340, “Top” 1420corresponds to top Edge Detector 1320, and “Side” 1430 corresponds toside Edge Detector 1330.

Using inverted AND Gate 1412 and AND Gate 1410 wired as shown in FIG.14, EventDetect Judge 1400 receives an event detection weight thatrepresents the level of confidence that object 1300 is not at theposition specified by at least one of top Edge Detector 1320 and sideEdge Detector 1330 at the time that labeling arm 162 is at the apex ofits stroke. An event condition for EventDetect Judge 1400 suitable forthis configuration would be “n>=2”.

When an event is detected, images may be recorded as further describedbelow. Furthermore, the logic output of EventDetect Judge 1400 is wiredto an Output gadget 1480, named “Signal”, which controls an outputsignal from the vision detector than can if desired be connected toautomation equipment such as a PLC or actuator. The Output Gadget 1280is configured by a user as appropriate, as further described in VisionDetector Method and Apparatus.

FIG. 15 shows an image view and a corresponding logic view that togetherprovides yet another configuration of a vision detector to detect anevent that might be useful for the production setup shown in FIG. 1. Inthis example, an event occurs when labeling arm 162 under- orover-extends, meaning that the apex of the stroke is in the wrong place,or when it extends to the correct position but remains there either toobriefly or too long for correct label application.

Overextension is easy to detect. An Edge Detector 1512 is placed belowthe expected apex of the downward stroke of labeling arm 162.Corresponding logic view Edge Detector “Hyper” 1540 is wired to“HyperEvent” EventDetect Judge 1570, which might use the event condition“w>=0.95” to detect an overextended arm. Note that this event condition,using total event detection weight w, would accept a single frame assufficient evidence if the event detection weight for that frame showsvery high confidence, but would require at least two frames if the eventdetection weights show lower confidence.

For the other conditions, a Locator 1500 is placed to detect thatlabeling arm 162 is within some range of positions near the apex, and anEdge Detector 1510 is placed to detect that labeling arm 162 is at theapex. Corresponding logic view Locator “Stroke” 1520 and inverted EdgeDetector “Apex” 1530 are wired as shown to AND Gate 1550, which is inturn wired to EventDetect Judge “StrokeEvent” 1560. The event detectionweight in this configuration represents a level of confidence thatlabeling arm 162 is near, but not at, the apex of its stroke.

Note that the logic view of FIG. 15 includes the use of two EventDetectJudges. When more than one EventDetect Judge is used, each operatesindependently so that an event is detected when any of the Judges findssufficient evidence. Each Judge would perform its own activity analysisstep 312 and event analysis step 314, using its own copy of active flag300, active statistics 302, and inactive statistics 304. Note that thevisual analysis steps 310 are performed by other Gadgets, such as Photosand Gates.

FIG. 16 shows how an event condition is formulated for EventDetect Judge“StrokeEvent” 1560 that is suitable for the example configuration ofFIG. 15. Shown are four plots of event detection weight d_(i) versusframe count i, similar to the plot shown in FIG. 4 that was describedabove.

First plot 1600 shows an arm that has under-extended. The arm movedclose to the desired apex for about a dozen frames, but never actuallyreached the apex. Second plot 1610 shows an arm that has extended to thedesired apex, but remained there too briefly, only about one frame, forcorrect label application. Third plot 1620 shows an arm that extendedcorrectly, reaching the desired apex and remaining there for about threeframes. Fourth plot 1630 shows an arm that extended too long, remainingat the desired apex for about seven frames.

The event condition of FIG. 10, “n>=2 & (a<0.50|a>0.75)”, is suitablefor detecting first plot 1600, second plot 1610, and fourth plot 1630,but not detecting third plot 1620 that corresponds to correct armextension. The “a<0.50” term detects first plot 1600 and second plot1610. The “a>0.75” term detects fourth plot 1630. The “n>=2” terminsures that a single-frame spurious event is not detected.

Clearly there are many other configurations and event conditions thatwould also be suitable for detecting mis-extension of a mechanicalcomponent such as labeling arm 162, and that will occur to those skilledin the art.

FIG. 17 illustrates one way to configure the invention to detect andrecord images of flaws on a continuous web. Image view 1710 shows aportion of continuous web 1700 that is moving past the vision detector.

Locator 1720 and Edge Detector 1722 are configured to inspect the web.If the web breaks, folds over, or becomes substantially frayed at eitheredge, then Locator 1720 and/or Edge Detector 1722 will produce a falseoutput (logic value<0.5). If the web moves up or down Locator 1720 willtrack the top edge and keep Edge Detector 1722 in the right relativeposition to detect the bottom edge. However, if the width of the webchanges substantially, Edge Detector 1742 will produce a false output.

In a logic view “Top” Locator 1740 represents Locator 1720, and “Bottom”Detector 1750 represents Edge Detector 1722. These are wired to AND Gate1760, whose logic output is inverted and wired to EventDetect Judge1770.

Marking, Stroke Events, and Synchronized Outputs

FIG. 18 shows a timing diagram that will be used to explain how visiondetector output signals may be synchronized with the mark time. Signalsynchronization is desirable for a variety of industrial inspectionpurposes, such as control of a downstream actuator.

Visual event detection is a novel capability and suggests novel outputsignal control. It is desirable that a vision detector be able tocontrol some external actuator, either directly or by serving as inputto a PLC. This suggests that the timing of output signals be relatedwith reasonable precision to a point in time with some physical meaning,such as when an object passes a particular, fixed point in theproduction flow (a flow event), or when a mechanical component reachesthe apex of a stroke (a stroke event). In the example of FIG. 1 a fixedpoint could be mark point 150, and in the timeline of FIG. 2 the time ismark times 250 and 252. In the example of FIG. 15, Edge Detector 1510 ispositioned at the apex of the stroke of labeling arm 162. In FIG. 18,the time is mark time 1800. Note that an encoder count may be usedinstead of time.

The present invention can provide outputs synchronized to reasonableprecision with the mark time, whether it controls an actuator directlyor is used by a PLC or any other external device. One problem, however,is that the present invention detects an event many milliseconds afterit occurs, i.e. many milliseconds after the mark time. Furthermore, thedelay may be quite variable, depending on how many frames were analyzedand, to a lesser extent, when in the capture/analyze cycle the mark timeoccurs.

FIG. 18 shows the EventDetect logic output 1840. A detect pulse 1870appears on EventDetect logic output 1840 when the decision is made atdecision point 1810. Decision point 1810 corresponds to the point intime when event block 372 in the flowchart of FIG. 3 is executed. Notethat the decision delay 1830 from mark time 1800 to the decision point1810 will be variable, depending on how many frames were analyzed and,to a lesser extent, when in the capture/analyze cycle the mark timeoccurs. Therefore the timing of detect pulse 1870 does not conveyaccurate information about when the event occurred.

The problem of variable decision delay 1830 would apply to any devicethat attempts to detect events by capturing and analyzing images, andwhere it is desired to provide a signal indicating when the eventoccurred to an accuracy that is better than the frame period (inverse ofthe frame rate). The invention solves the problem by measuring the marktime 1800 and then synchronizing an output pulse 1880 on output signal1860 to it. The output pulse 1880 occurs at a fixed output delay 1820from mark time 1800.

The act of measuring the mark time is called marking. The mark time canbe determined to an accuracy significantly better than the frame periodby linear interpolation, least-squares fit, or other well-known methods,using the known times (or encoder counts) at which the images werecaptured and the known positions of objects, mechanical components, oranything moving in the field of view, as determined by appropriateLocators. Accuracy will depend on shutter time, overall capture/analysiscycle time, speed of motion, and other factors.

In an illustrative embodiment a user chooses one Locator whose searchrange is substantially along the direction of motion to be used formarking. For flow events the mark point is arbitrarily chosen to be thecenter point of the Locator's range—as discussed above, the mark pointis an imaginary reference point whose exact position doesn't matter aslong as it is fixed. The user can achieve the desired synchronization ofoutput signals by adjusting the delay from this arbitrary time. If anevent is detected but the motion does not cross the mark point duringthe active frames, the mark time can be based on an extrapolation andthe accuracy may suffer. For stroke events the mark point is the apex ofthe stroke, measured as described below. Clearly, other definitions ofthe mark point can be used to practice the invention.

Note that output signals can only be synchronized to the mark time ifoutput delay 1820 is longer than the longest expected decision delay1830. Thus any action taken as a result of output pulse 1880, forexample operation of an actuator, should be sufficiently downstream ofthe mark point, which is expected to be the case in almost allapplications.

FIG. 19 shows a plot of Locator results as a function of time for astroke event. The Locator must be configured to search in a directionsubstantially parallel to the stroke direction, for example Locator 1500in FIG. 15. Note that a multi-dimensional Locator could also be used, aslong as the dimensions searched includes a direction substantiallyparallel to the stroke direction.

Time, measured from an arbitrary reference point, is plotted onhorizontal axis 1900.

The logic output of the Locator is plotted as a sequence ofdiamond-shaped points, including outline example point 1920 and solidexample point 1922, connected by Locator position curve 1950. Note thatthe Locator measures position only at the discrete times where thediamond-shaped points are plotted, which correspond to frames, andtherefore Locator position curve 1950 should be understood to be drawnfor the convenience of the reader and does not represent continuousmeasurements by the Locator. Logic output values, corresponding to thevertical position of the diamond-shaped points, are plotted on firstvertical axis 1910. Diamond points drawn in outline, including outlineexample point 1920, signify low logic output values (below 0.5,corresponding to reference line 1914 in the illustrated embodiment)where there is little confidence that the Locator has found the intendedimage feature. Diamond points drawn solid, including solid example point1922, signify high logic output values (at or above 0.5 in theillustrated embodiment) where there is strong confidence that theLocator has found the intended image feature, and therefore that itsmeasured position is valid.

The measured position of the Locator is plotted as a sequence ofposition points drawn as solid circles, including example position point1930. Position values, corresponding to the vertical position of theposition points, are plotted on second vertical axis 1912 and aremeasured in pixels from the center of the Locator. Note that positionpoints are only shown for frames where the logic output of the Locatoris at or above 0.5, i.e. those frames for which there is strongconfidence that the Locator has found the intended image feature.

It can be seen by examining the position points that a mechanicalcomponent in the field of view has advanced through the search range ofthe Locator for about four frames to an apex at around +6 pixels, hasheld at that apex for around six frames, and then retreated for anotherfour frames before traveling beyond the search range. To compute aspecific mark time for this stroke event, the illustrated embodimentuses the position points to compute a best-fit parabola 1940, from whichthe apex of the parabola 1960 can easily be determined. In theillustrated example, the mark time (apex of the best-fit parabola)occurs at 19.2 milliseconds.

Methods for computing a best-fit parabola from a set of points arewell-known in the art. It will be obvious to one skilled in the art thatother methods for determining the apex of a stroke event can be usedwithin the scope of the invention. Furthermore, it will be obvious thatmotions other than the flow and stroke events considered herein can betracked using methods herein described, and that appropriate curves canbe fit, or other techniques used, to determine a mark time for suchmotions.

FIG. 20 shows a parameter view for user configuration of an OutputGadget, including controls to set output delay 1820 (FIG. 18). Modecontrol 2000 allows a user to choose how the output signal iscontrolled. In “straight through” mode, the logic input is passeddirectly to the output signal without any delay or synchronization. In“delayed” mode, on the rising edge of the logic input an output pulse isscheduled to occur at a time delayed from the most recently measuredmark time (or encoder count) by the amount specified by delay controls2010, and of duration specified by pulse controls 2020. The scheduledpulse may be placed in a FIFO associated with the Output Gadget.

Recording and Retrieval of Images

FIG. 21 shows details of the organization of a portion of memory 710(FIG. 7) used in an illustrative embodiment. A frame buffer pool 2100contains a number of individual frame buffers, such as frame buffers2130, 2132, 2134, 2136, and 2138, to be used for various purposes. Afree pool 2110 is organized as a ring buffer and used to capture andanalyze frames for event detection. Write pointer 2120 indicates thenext available frame buffer 2130, into which the next frame is captured.Simultaneously with image capture into frame buffer 2130, the previousimage in frame buffer 2132 is being analyzed. At some point the ringbuffer may become full, at which point the oldest frames will beoverwritten.

In an illustrative embodiment where the imager 760 is an LM9630, forexample, each frame buffer would contain 128×100 8-bit pixels. Forclarity in the drawing frame buffer pool 2100 is shown to contain only afew dozen elements, but in practice a higher number is desirable. In oneembodiment 160 elements are used, which requires just under twomegabytes of storage, and which is capable of storing about 0.8 secondsof a production run at 200 frames/second, or about 0.32 seconds at 500frames/second. Clearly, lower frame rates can be used to increase theamount of time for which images can be stored.

When an event is detected, which may happen many frames after the eventoccurs, a recent history of captured images will be in free pool 2110.In an illustrative embodiment, free pool 2110 is large enough to holdthe event frames and frames prior to and after the event frames insufficient number for the purposes of the application. At the time anevent is detected the recent history may contain none, some, or all ofthe frames to be recorded, depending on user choices such as recordingcontrols 1050. If the recent history contains all of the frames, theycan be recorded immediately as described below. If not, recordinghappens at a time in the future when all of the frames to be recordedare available.

In the illustrated example, frame buffers marked “R”, including example2134, hold images to be recorded, and “E”, including example 2136, holdevent frames (also to be recorded). The event occurs at mark time 2160,and is detected during the analysis of the frame in buffer 2134. At thetime the event was detected, some but not all of the frames to berecorded are in the recent history. During later analysis of the framein buffer 2132, it is determined that the recent history now containsall of the frames to be recorded.

To record the frames, the frame buffers are removed from free pool 2110and added to stored event pool 2104, which includes stored events 2112,2114, and 2116. If the number of frame buffers in free pool 2110 becomestoo small after removing the new stored event, various actions arepossible. In one embodiment, event detection ceases until HMI 630 (FIG.6) uploads the frames in stored event pool 2104 so that the buffers canbe returned to free pool 2110. In another embodiment, one or more olderstored events may be taken from stored event pool 2104 and placed backin free pool 2104. Those older events will no longer be available fordisplay.

In an illustrative embodiment, frame buffers are never copied. Insteadframe buffers are moved between free pool 2110 and stored event pool2104 by pointer manipulation using techniques well known in the art.

A list of stored events 2102 is maintained, including list elements2140, 2142, and 2144. List element 2140, for example, contains nextelement pointer 2150, frame buffer count 2152, result information 2154,and stored event pointer 2156. Result information 2154 may include atimestamp, as illustrated, or other information not shown, such asactive statistics 302.

Result information 2154 includes information that applies to the eventas a whole. It is further desirable to provide information for eachrecorded frame, examples of which are shown in the frame buffers ofstored event pool 2104. In the illustrated examples, the storedinformation includes a timestamp that records the capture time of theframe in milliseconds relative to the mark time. Other information (notshown), such as the event detection weight and individual Gadgetresults, may be recorded as well.

Referring back to FIG. 6, the vision detector may be connected to ahuman-machine interface (HMI) 630, via signals 640, for purposes ofconfiguration. It is also possible for the HMI to be part of the visiondetector 600, but this is less preferred because the HMI is generallynot needed for event detection, and so one HMI can be shared among manyvision detectors. The HMI may run a graphical user interface (GUI) ofconventional design, an illustrative portion of which is shown in FIG.22.

The GUI allows a portion of the recorded images stored in visiondetector memory 710 to be displayed for a human user. In theillustrative embodiment of FIG. 22, a filmstrip window 2202 displays upto eight thumbnail images 2210, 2212, 2214, 2216,2220, 2230, 2232, and2234, each thumbnail image being a low-resolution version of acorresponding recorded image from stored event pool 2104. Generally thethumbnail images correspond to consecutive images of a single event inthe record, but other arrangements may be useful, such as skipping somenumber of images between the corresponding thumbnails.

A set of scrolling controls 2250 is provided in filmstrip window 2202for advancing the thumbnail images forward or backward within therecorded images of an event, and between events. Next image control 2260advances forward by one image, and previous image control 2262 advancesbackward by one image. Next event control 2264 and previous eventcontrol 2266 advance the display forward and backward by one event.

Thumbnail 2220 displays a low-resolution image of object 2242, which maycorrespond for example to object 116 (FIG. 1). Object 2242 also appearsin all of the other thumbnails, for example object 2240 in thumbnail2210, at slightly different viewing perspectives (positions within thefield of view) and at different times during the application of label2270 by arm 2272. By issuing scrolling commands using scrolling controls2250 the user can advance the recorded images forward or backward to seeany desired time interval. Considering the illustrated example images inthumbnails 2212, 2214, and 2216 in particular, in appears that label2270 may have snagged on the top edge of the object as it was beingapplied.

In the illustrative embodiment of FIG. 22, the image corresponding tothumbnail 2220, which is shown with a heavy outline and referred to asthe selected image, is also displayed at full resolution in image viewwindow 2200. As scrolling commands advance the displayed portion forwardand backward, different selected images will move into thumbnail 2220and be displayed at full resolution in image view window 2200. Otherinformation about the selected image may also be displayed, such as timestamp 2280 that indicates the capture time of the selected image (inmilliseconds in this example) relative to the mark time.

The foregoing has been a detailed description of various embodiments ofthe invention. It is expressly contemplated that a wide range ofmodifications and additions can be made hereto without departing fromthe spirit and scope of this invention. For example, the processors andcomputing devices herein are exemplary and a variety of processors andcomputers, both standalone and distributed can be employed to performcomputations herein. Likewise, the imager and other vision componentsdescribed herein are exemplary and improved or differing components canbe employed within the teachings of this invention. The softwareelements, GUI designs and layouts, parameter values, and mathematicalformulas can all be modified or replaced with equivalents as appropriatefor specific applications of the invention. Accordingly, thisdescription is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

1-16. (canceled)
 17. A method for automatic visual detection of anevent, comprising: capturing a plurality of frames, each frame in theplurality of frames comprising an image of a two-dimensional field ofview in which the event occurs, the event comprising a motion of apredetermined object; computing, responsive to a visual analysis of theplurality of frames, a plurality of event detection weights, each eventdetection weight of the plurality of event detection weightscorresponding respectively to each frame of the plurality of frames andcomprising evidence that the event is occurring in the field of view;selecting a plurality of event frames from the plurality of frames, theplurality of event frames corresponding to a subset of the plurality ofevent detection weights; and determining, responsive to an eventanalysis of the subset of the plurality of event detection weights,whether the event has occurred.
 18. A method for automatic visualdetection of an event, comprising: capturing a plurality of frames, eachframe in the plurality of frames comprising an image of atwo-dimensional field of view in which the event occurs, the eventcomprising a repetitive motion; computing, responsive to a visualanalysis of the plurality of frames, a plurality of event detectionweights, each event detection weight of the plurality of event detectionweights corresponding respectively to each frame of the plurality offrames and comprising evidence that the event is occurring in the fieldof view; selecting a plurality of event frames from the plurality offrames, the plurality of event frames corresponding to a subset of theplurality of event detection weights; and determining, responsive to anevent analysis of the subset of the plurality of event detectionweights, whether the event has occurred.
 19. A method for automaticvisual detection of an event, comprising: capturing a plurality offrames, each frame in the plurality of frames comprising an image of atwo-dimensional field of view in which the event occurs, the eventcomprising a motion across a fixed reference point; computing,responsive to a visual analysis of the plurality of frames, a pluralityof event detection weights, each event detection weight of the pluralityof event detection weights corresponding respectively to each frame ofthe plurality of frames and comprising evidence that the event isoccurring in the field of view; selecting a plurality of event framesfrom the plurality of frames, the plurality of event framescorresponding to a subset of the plurality of event detection weights;and determining, responsive to an event analysis of the subset of theplurality of event detection weights, whether the event has occurred.20. A method for automatic visual detection of an event, comprising:capturing a plurality of frames, each frame in the plurality of framescomprising an image of a two-dimensional field of view in which theevent occurs, the event comprising a motion of advance and retreat;computing, responsive to a visual analysis of the plurality of frames, aplurality of event detection weights, each event detection weight of theplurality of event detection weights corresponding respectively to eachframe of the plurality of frames and comprising evidence that the eventis occurring in the field of view; selecting a plurality of event framesfrom the plurality of frames, the plurality of event framescorresponding to a subset of the plurality of event detection weights;and determining, responsive to an event analysis of the subset of theplurality of event detection weights, whether the event has occurred.21. A method for automatic visual detection of an event, comprising:capturing a plurality of frames, each frame in the plurality of framescomprising an image of a two-dimensional field of view in which theevent occurs, the event comprising an incorrectly manufactured objectpassing through the field of view; computing, responsive to a visualanalysis of the plurality of frames, a plurality of event detectionweights, each event detection weight of the plurality of event detectionweights corresponding respectively to each frame of the plurality offrames and comprising evidence that the event is occurring in the fieldof view; selecting a plurality of event frames from the plurality offrames, the plurality of event frames corresponding to a subset of theplurality of event detection weights; and determining, responsive to anevent analysis of the subset of the plurality of event detectionweights, whether the event has occurred.
 22. A method for automaticvisual detection of an event, comprising: capturing a plurality offrames, each frame in the plurality of frames comprising an image of atwo-dimensional field of view in which the event occurs, the eventcomprising an incorrect motion of an object in the field of view;computing, responsive to a visual analysis of the plurality of frames, aplurality of event detection weights, each event detection weight of theplurality of event detection weights corresponding respectively to eachframe of the plurality of frames and comprising evidence that the eventis occurring in the field of view; selecting a plurality of event framesfrom the plurality of frames, the plurality of event framescorresponding to a subset of the plurality of event detection weights;and determining, responsive to an event analysis of the subset of theplurality of event detection weights, whether the event has occurred.23. The method of claim 22, wherein the incorrect motion comprises amotion of the object that is insufficient for the object to reach aregion of the field of view.
 24. The method of claim 22, wherein theincorrect motion comprises a motion of the object that is significantlybeyond a region of the field of view.
 25. The method of claim 22,wherein the incorrect motion comprises a motion wherein the objectremains in a region of the field of view for an insufficient interval oftime.
 26. The method of claim 22, wherein the incorrect motion comprisesa motion wherein the object remains in a region of the field of view foran excessive interval of time.
 27. A method for automatic visualdetection of an event, comprising: capturing a plurality of frames, eachframe in the plurality of frames comprising an image of atwo-dimensional field of view in which the event occurs, the eventcomprising an incorrectly positioned object; computing, responsive to avisual analysis of the plurality of frames, a plurality of eventdetection weights, each event detection weight of the plurality of eventdetection weights corresponding respectively to each frame of theplurality of frames and comprising evidence that the event is occurringin the field of view; selecting a plurality of event frames from theplurality of frames, the plurality of event frames corresponding to asubset of the plurality of event detection weights; and determining,responsive to an event analysis of the subset of the plurality of eventdetection weights, whether the event has occurred.
 28. A method forautomatic visual detection of an event, comprising: capturing aplurality of frames, each frame in the plurality of frames comprising animage of a two-dimensional field of view in which the event occurs, theevent comprising a substantial change in a dimension of a continuousweb; computing, responsive to a visual analysis of the plurality offrames, a plurality of event detection weights, each event detectionweight of the plurality of event detection weights correspondingrespectively to each frame of the plurality of frames and comprisingevidence that the event is occurring in the field of view; selecting aplurality of event frames from the plurality of frames, the plurality ofevent frames corresponding to a subset of the plurality of eventdetection weights; and determining, responsive to an event analysis ofthe subset of the plurality of event detection weights, whether theevent has occurred.
 29. A method for automatic visual detection of anevent, comprising: capturing a plurality of frames, each frame in theplurality of frames comprising an image of a two-dimensional field ofview in which the event occurs; computing, responsive to a visualanalysis of the plurality of frames, a plurality of event detectionweights, each event detection weight of the plurality of event detectionweights corresponding respectively to each frame of the plurality offrames and comprising evidence that the event is occurring in the fieldof view; selecting a plurality of event frames from the plurality offrames, the plurality of event frames corresponding to a subset of theplurality of event detection weights; determining, responsive to anevent analysis of the subset of the plurality of event detectionweights, whether the event has occurred; and producing a signal that issynchronized with a time at which the event occurs.
 30. A method forautomatic visual detection of an event, comprising: capturing aplurality of frames, each frame in the plurality of frames comprising animage of a two-dimensional field of view in which the event occurs;computing, responsive to a visual analysis of the plurality of frames, aplurality of event detection weights, each event detection weight of theplurality of event detection weights corresponding respectively to eachframe of the plurality of frames and comprising evidence that the eventis occurring in the field of view; selecting a plurality of event framesfrom the plurality of frames, the plurality of event framescorresponding to a subset of the plurality of event detection weights;and determining, responsive to an event analysis of the subset of theplurality of event detection weights, whether the event has occurred,the event analysis comprising a plurality of comparison operationsproducing a corresponding plurality of logical results, each comparisonoperation of the plurality of comparison operations comprising acomparison of a first value and a second value, wherein at least one ofthe first value and the second value are responsive to a numericalstatistic of the subset of the plurality of event detection weights; andat least one logical operation that combines the plurality of logicalresults to determine whether the event has occurred.
 31. A method forautomatic visual detection of an event, comprising: capturing aplurality of frames, each frame in the plurality of frames comprising animage of a two-dimensional field of view in which the event occurs;computing, responsive to a visual analysis of the plurality of frames, aplurality of event detection weights, each event detection weight of theplurality of event detection weights corresponding respectively to eachframe of the plurality of frames and comprising evidence that the eventis occurring in the field of view; selecting a plurality of event framesfrom the plurality of frames, the plurality of event framescorresponding to a subset of the plurality of event detection weights;and determining, responsive to an event analysis of the subset of theplurality of event detection weights, whether the event has occurred,the event analysis comprising a text string representing an expressionin a syntax substantially similar to the syntax of a conventionalprogramming language.
 32. A method for automatic visual detection of anevent, comprising: capturing a plurality of frames, each frame in theplurality of frames comprising an image of a two-dimensional field ofview in which the event occurs, the field of view comprising no morethan about 40,000 pixels; computing, responsive to a visual analysis ofthe plurality of frames, a plurality of event detection weights, eachevent detection weight of the plurality of event detection weightscorresponding respectively to each frame of the plurality of frames andcomprising evidence that the event is occurring in the field of view;selecting a plurality of event frames from the plurality of frames, theplurality of event frames corresponding to a subset of the plurality ofevent detection weights; and determining, responsive to an eventanalysis of the subset of the plurality of event detection weights,whether the event has occurred.
 33. A method for automatic visualdetection of an event, comprising: capturing a plurality of frames, eachframe in the plurality of frames comprising an image of atwo-dimensional field of view in which the event occurs; computing,responsive to a visual analysis of the plurality of frames, a pluralityof event detection weights, each event detection weight of the pluralityof event detection weights corresponding respectively to each frame ofthe plurality of frames and comprising evidence that the event isoccurring in the field of view; selecting a plurality of event framesfrom the plurality of frames, the plurality of event framescorresponding to a subset of the plurality of event detection weights;determining, responsive to an event analysis of the subset of theplurality of event detection weights, whether the event has occurred;and wherein the steps of the method are performed at a rate of not lessthan two hundred frames per second.
 34. A method for automatic visualdetection of an event, comprising: capturing a plurality of frames, eachframe in the plurality of frames comprising an image of atwo-dimensional field of view in which the event occurs; computing,responsive to a visual analysis of the plurality of frames, a pluralityof event detection weights, each event detection weight of the pluralityof event detection weights corresponding respectively to each frame ofthe plurality of frames and comprising evidence that the event isoccurring in the field of view, the visual analysis comprising, for eachframe in the plurality of frames, using a locator to adjust a positionof a region of interest in the field of view; and analyzing the regionof interest; selecting a plurality of event frames from the plurality offrames, the plurality of event frames corresponding to a subset of theplurality of event detection weights; and determining, responsive to anevent analysis of the subset of the plurality of event detectionweights, whether the event has occurred.
 35. A method for automaticvisual detection of an event, comprising: capturing a plurality offrames, each frame in the plurality of frames comprising an image of atwo-dimensional field of view in which the event occurs; computing,responsive to a visual analysis of the plurality of frames, a pluralityof event detection weights, each event detection weight of the pluralityof event detection weights corresponding respectively to each frame ofthe plurality of frames and comprising evidence that the event isoccurring in the field of view; selecting, responsive to an activityanalysis of the plurality of event detection weights, a plurality ofevent frames from the plurality of frames, the plurality of event framescorresponding to a subset of the plurality of event detection weights,the activity analysis comprising entering an active state when thevisual analysis computes an event detection weight that indicatessufficient evidence that the event is occurring; and exiting the activestate when the visual analysis computes a plurality of event detectionweights that each indicate insufficient evidence that the event isoccurring; and determining, responsive to an event analysis of thesubset of the plurality of event detection weights, whether the eventhas occurred.
 36. A method for automatic visual detection of an event,comprising: using a human-machine interface to specify the event, thehuman-machine interface comprising a logic view; capturing a pluralityof frames, each frame in the plurality of frames comprising an image ofa two-dimensional field of view in which the event occurs; computing,responsive to a visual analysis of the plurality of frames, a pluralityof event detection weights, each event detection weight of the pluralityof event detection weights corresponding respectively to each frame ofthe plurality of frames and comprising evidence that the event isoccurring in the field of view; selecting a plurality of event framesfrom the plurality of frames, the plurality of event framescorresponding to a subset of the plurality of event detection weights;and determining, responsive to an event analysis of the subset of theplurality of event detection weights, whether the event has occurred.37. A system for automatic visual detection of an event, comprising: acapture process that captures a plurality of frames, each frame in theplurality of frames comprising an image of a two-dimensional field ofview in which the event occurs, the event comprising a motion of apredetermined object; a visual analysis process that computes,responsive to the plurality of frames, a plurality of event detectionweights, each event detection weight of the plurality of event detectionweights corresponding respectively to each frame of the plurality offrames and comprising evidence that the event is occurring in the fieldof view; a selection process that selects a plurality of event framesfrom the plurality of frames, the plurality of event framescorresponding to a subset of the plurality of event detection weights;and an event analysis process that determines, responsive to the subsetof the plurality of event detection weights, whether the event hasoccurred.
 38. A system for automatic visual detection of an event,comprising: a capture process that captures a plurality of frames, eachframe in the plurality of frames comprising an image of atwo-dimensional field of view in which the event occurs, the eventcomprising a repetitive motion; a visual analysis process that computes,responsive to the plurality of frames, a plurality of event detectionweights, each event detection weight of the plurality of event detectionweights corresponding respectively to each frame of the plurality offrames and comprising evidence that the event is occurring in the fieldof view; a selection process that selects a plurality of event framesfrom the plurality of frames, the plurality of event framescorresponding to a subset of the plurality of event detection weights;and an event analysis process that determines, responsive to the subsetof the plurality of event detection weights, whether the event hasoccurred.
 39. A system for automatic visual detection of an event,comprising: a capture process that captures a plurality of frames, eachframe in the plurality of frames comprising an image of atwo-dimensional field of view in which the event occurs, the eventcomprising a motion across a fixed reference point; a visual analysisprocess that computes, responsive to the plurality of frames, aplurality of event detection weights, each event detection weight of theplurality of event detection weights corresponding respectively to eachframe of the plurality of frames and comprising evidence that the eventis occurring in the field of view; a selection process that selects aplurality of event frames from the plurality of frames, the plurality ofevent frames corresponding to a subset of the plurality of eventdetection weights; and an event analysis process that determines,responsive to the subset of the plurality of event detection weights,whether the event has occurred.
 40. A system for automatic visualdetection of an event, comprising: a capture process that captures aplurality of frames, each frame in the plurality of frames comprising animage of a two-dimensional field of view in which the event occurs, theevent comprising a motion of advance and retreat; a visual analysisprocess that computes, responsive to the plurality of frames, aplurality of event detection weights, each event detection weight of theplurality of event detection weights corresponding respectively to eachframe of the plurality of frames and comprising evidence that the eventis occurring in the field of view; a selection process that selects aplurality of event frames from the plurality of frames, the plurality ofevent frames corresponding to a subset of the plurality of eventdetection weights; and an event analysis process that determines,responsive to the subset of the plurality of event detection weights,whether the event has occurred.
 41. A system for automatic visualdetection of an event, comprising: a capture process that captures aplurality of frames, each frame in the plurality of frames comprising animage of a two-dimensional field of view in which the event occurs, theevent comprising an incorrectly manufactured object passing through thefield of view; a visual analysis process that computes, responsive tothe plurality of frames, a plurality of event detection weights, eachevent detection weight of the plurality of event detection weightscorresponding respectively to each frame of the plurality of frames andcomprising evidence that the event is occurring in the field of view; aselection process that selects a plurality of event frames from theplurality of frames, the plurality of event frames corresponding to asubset of the plurality of event detection weights; and an eventanalysis process that determines, responsive to the subset of theplurality of event detection weights, whether the event has occurred.42. A system for automatic visual detection of an event, comprising: acapture process that captures a plurality of frames, each frame in theplurality of frames comprising an image of a two-dimensional field ofview in which the event occurs, the event comprising an incorrect motionof an object in the field of view; a visual analysis process thatcomputes, responsive to the plurality of frames, a plurality of eventdetection weights, each event detection weight of the plurality of eventdetection weights corresponding respectively to each frame of theplurality of frames and comprising evidence that the event is occurringin the field of view; a selection process that selects a plurality ofevent frames from the plurality of frames, the plurality of event framescorresponding to a subset of the plurality of event detection weights;and an event analysis process that determines, responsive to the subsetof the plurality of event detection weights, whether the event hasoccurred.
 43. The system of claim 42, wherein the incorrect motioncomprises a motion of the object that is insufficient for the object toreach a region of the field of view.
 44. The system of claim 42, whereinthe incorrect motion comprises a motion of the object that issignificantly beyond a region of the field of view.
 45. The system ofclaim 42, wherein the incorrect motion comprises a motion wherein theobject remains in a region of the field of view for an insufficientinterval of time.
 46. The system of claim 42, wherein the incorrectmotion comprises a motion wherein the object remains in a region of thefield of view for an excessive interval of time.
 47. A system forautomatic visual detection of an event, comprising: a capture processthat captures a plurality of frames, each frame in the plurality offrames comprising an image of a two-dimensional field of view in whichthe event occurs, the event comprising an incorrectly positioned object;a visual analysis process that computes, responsive to the plurality offrames, a plurality of event detection weights, each event detectionweight of the plurality of event detection weights correspondingrespectively to each frame of the plurality of frames and comprisingevidence that the event is occurring in the field of view; a selectionprocess that selects a plurality of event frames from the plurality offrames, the plurality of event frames corresponding to a subset of theplurality of event detection weights; and an event analysis process thatdetermines, responsive to the subset of the plurality of event detectionweights, whether the event has occurred.
 48. A system for automaticvisual detection of an event, comprising: a capture process thatcaptures a plurality of frames, each frame in the plurality of framescomprising an image of a two-dimensional field of view in which theevent occurs, the event comprising a substantial change in a dimensionof a continuous web; a visual analysis process that computes, responsiveto the plurality of frames, a plurality of event detection weights, eachevent detection weight of the plurality of event detection weightscorresponding respectively to each frame of the plurality of frames andcomprising evidence that the event is occurring in the field of view; aselection process that selects a plurality of event frames from theplurality of frames, the plurality of event frames corresponding to asubset of the plurality of event detection weights; and an eventanalysis process that determines, responsive to the subset of theplurality of event detection weights, whether the event has occurred.49. A system for automatic visual detection of an event, comprising: acapture process that captures a plurality of frames, each frame in theplurality of frames comprising an image of a two-dimensional field ofview in which the event occurs; a visual analysis process that computes,responsive to the plurality of frames, a plurality of event detectionweights, each event detection weight of the plurality of event detectionweights corresponding respectively to each frame of the plurality offrames and comprising evidence that the event is occurring in the fieldof view; a selection process that selects a plurality of event framesfrom the plurality of frames, the plurality of event framescorresponding to a subset of the plurality of event detection weights;an event analysis process that determines, responsive to the subset ofthe plurality of event detection weights, whether the event hasoccurred; and an output process that produces a signal that issynchronized with a time at which the event occurs.
 50. A system forautomatic visual detection of an event, comprising: a capture processthat captures a plurality of frames, each frame in the plurality offrames comprising an image of a two-dimensional field of view in whichthe event occurs; a visual analysis process that computes, responsive tothe plurality of frames, a plurality of event detection weights, eachevent detection weight of the plurality of event detection weightscorresponding respectively to each frame of the plurality of frames andcomprising evidence that the event is occurring in the field of view; aselection process that selects a plurality of event frames from theplurality of frames, the plurality of event frames corresponding to asubset of the plurality of event detection weights; and an eventanalysis process that determines, responsive to the subset of theplurality of event detection weights, whether the event has occurred,the event analysis process comprising a plurality of comparisonoperations producing a corresponding plurality of logical results, eachcomparison operation of the plurality of comparison operationscomprising a comparison of a first value and a second value, wherein atleast one of the first value and the second value are responsive to anumerical statistic of the subset of the plurality of event detectionweights; and at least one logical operation that combines the pluralityof logical results to determine whether the event has occurred.
 51. Asystem for automatic visual detection of an event, comprising: a captureprocess that captures a plurality of frames, each frame in the pluralityof frames comprising an image of a two-dimensional field of view inwhich the event occurs; a visual analysis process that computes,responsive to the plurality of frames, a plurality of event detectionweights, each event detection weight of the plurality of event detectionweights corresponding respectively to each frame of the plurality offrames and comprising evidence that the event is occurring in the fieldof view; a selection process that selects a plurality of event framesfrom the plurality of frames, the plurality of event framescorresponding to a subset of the plurality of event detection weights;and an event analysis process that determines, responsive to the subsetof the plurality of event detection weights, whether the event hasoccurred, the event analysis process comprising a text stringrepresenting an expression in a syntax substantially similar to thesyntax of a conventional programming language.
 52. A system forautomatic visual detection of an event, comprising: a capture processthat captures a plurality of frames, each frame in the plurality offrames comprising an image of a two-dimensional field of view in whichthe event occurs, the capture process comprising an imager comprising nomore than about 40,000 pixels; a visual analysis process that computes,responsive to the plurality of frames, a plurality of event detectionweights, each event detection weight of the plurality of event detectionweights corresponding respectively to each frame of the plurality offrames and comprising evidence that the event is occurring in the fieldof view; a selection process that selects a plurality of event framesfrom the plurality of frames, the plurality of event framescorresponding to a subset of the plurality of event detection weights;and an event analysis process that determines, responsive to the subsetof the plurality of event detection weights, whether the event hasoccurred.
 53. A system for automatic visual detection of an event,comprising: a capture process that captures a plurality of frames, eachframe in the plurality of frames comprising an image of atwo-dimensional field of view in which the event occurs; a visualanalysis process that computes, responsive to the plurality of frames, aplurality of event detection weights, each event detection weight of theplurality of event detection weights corresponding respectively to eachframe of the plurality of frames and comprising evidence that the eventis occurring in the field of view; a selection process that selects aplurality of event frames from the plurality of frames, the plurality ofevent frames corresponding to a subset of the plurality of eventdetection weights; an event analysis process that determines, responsiveto the subset of the plurality of event detection weights, whether theevent has occurred; and wherein the capture process, the visual analysisprocess, the selection process, and the event analysis process operateat a rate of not less than two hundred frames per second.
 54. A systemfor automatic visual detection of an event, comprising: a captureprocess that captures a plurality of frames, each frame in the pluralityof frames comprising an image of a two-dimensional field of view inwhich the event occurs; a visual analysis process that computes,responsive to the plurality of frames, a plurality of event detectionweights, each event detection weight of the plurality of event detectionweights corresponding respectively to each frame of the plurality offrames and comprising evidence that the event is occurring in the fieldof view, the visual analysis process comprising, for each frame in theplurality of frames, a locator that adjusts a position of a region ofinterest in the field of view; and a region analysis process thatanalyzes the region of interest; a selection process that selects aplurality of event frames from the plurality of frames, the plurality ofevent frames corresponding to a subset of the plurality of eventdetection weights; and an event analysis process that determines,responsive to the subset of the plurality of event detection weights,whether the event has occurred.
 55. A system for automatic visualdetection of an event, comprising: a capture process that captures aplurality of frames, each frame in the plurality of frames comprising animage of a two-dimensional field of view in which the event occurs; avisual analysis process that computes, responsive to the plurality offrames, a plurality of event detection weights, each event detectionweight of the plurality of event detection weights correspondingrespectively to each frame of the plurality of frames and comprisingevidence that the event is occurring in the field of view; an activityanalysis process that selects, responsive to the plurality of eventdetection weights, a plurality of event frames from the plurality offrames, the plurality of event frames corresponding to a subset of theplurality of event detection weights, the activity analysis processcomprising an active state that is entered when the visual analysisprocess computes an event detection weight that indicates sufficientevidence that the event is occurring, and that is exited when the visualanalysis process computes a plurality of event detection weights thateach indicate insufficient evidence that the event is occurring; and anevent analysis process that determines, responsive to the subset of theplurality of event detection weights, whether the event has occurred.56. A system for automatic visual detection of an event, comprising: ahuman-machine interface that specifies the event, the human-machineinterface comprising a logic view; a capture process that captures aplurality of frames, each frame in the plurality of frames comprising animage of a two-dimensional field of view in which the event occurs; avisual analysis process that computes, responsive to the plurality offrames, a plurality of event detection weights, each event detectionweight of the plurality of event detection weights correspondingrespectively to each frame of the plurality of frames and comprisingevidence that the event is occurring in the field of view; a selectionprocess that selects a plurality of event frames from the plurality offrames, the plurality of event frames corresponding to a subset of theplurality of event detection weights; and an event analysis process thatdetermines, responsive to the subset of the plurality of event detectionweights, whether the event has occurred.